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To accurately forecast crude oil futures price and volatility, this article presents a novel deep learning combined model using secondary decomposition with West Texas Intermediate crude oil futures (WTI) and North Sea Brent crude oil futures (Brent) as examples. Firstly, a trend subsequence and several noise subsequences are obtained by decomposing the crude oil futures price or volatility using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the secondary decomposition is performed on the highest frequency noise subsequence using the variational mode decomposition (VMD). Secondly, the remaining subsequences obtained from CEEMDAN and the subsequences obtained from VMD are predicted separately using the BiGRU-Attention-CNN model. Finally, the predicted crude oil futures price or volatility is calculated by linearly integrating the predicted values of each subsequence. The empirical analysis shows that the novel combined model using secondary decomposition proposed in this paper has the best forecasting performance among many models, both in the comparison experiments and in the ablation experiments. The model is also shown to have good robustness by predicting the volatility at different maturities, varying the ratio of the training set and the test set for crude oil futures price prediction, and predicting the price of crude oil futures after extreme events. Overall, the novel combined forecasting model using secondary decomposition proposed in this paper can help countries grasp the direction of the crude oil market and improve national economic and political security. [ABSTRACT FROM AUTHOR]

In the increasingly complex urban transportation landscape, the necessity for accurate, multi-step forecasting has never been more apparent to help plan for long-term, strategic transportation initiatives like Intelligent Transportation Systems. This work focuses on resolving uncertainties around the proper training step size for machine learning models performing multi-step traffic flow forecasting. Two strategies are considered: one-step and horizon-step training sizes, which extend from the single-step forecasting method. This paper compares and evaluates two machine learning models: the Support Vector Regression and Long Short-Term Memory (LSTM). Data from two country road locations, including urban roads in Kuala Lumpur and the I5-North freeway in California, are employed to forecast traffic flow in 1-hour increments, projecting from 2 h up to 24 h ahead. The results reveal a significant difference in performance between the two training step sizes, with the one-step training size emerging as the more consistent and optimal strategy for both Direct and Multi-Input Multi-Output forecasting strategies. The proposed model, the Direct Particle Swarm Optimization Grid Search Support Vector Regression (Direct-PSOGS-SVR) model, exhibited comparable or slightly better performance than the LSTM model in both environments. In the I5-North freeway dataset, the Direct-PSOGS-SVR achieved similar Root Mean Squared Error values across most forecasting tasks compared to the LSTM, indicating its effectiveness in stable traffic flow patterns. Notably, in the more dynamic urban environment of Kuala Lumpur, the Direct-PSOGS-SVR model also demonstrated stability and resilience in forecasting accuracy, effectively handling the inherent noise and fluctuations in traffic patterns. These findings serve as a valuable guide for practitioners in selecting the most efficacious combination of training strategies for specific time series forecasting tasks utilizing machine learning models, particularly in traffic flow forecasting. Introducing the Direct-PSOGS-SVR model enriches the landscape of machine learning solutions for traffic forecasting, underscoring the paper's contributions to the broader understanding of time series forecasting dynamics. [ABSTRACT FROM AUTHOR]

Long time series forecasting is widely used in areas such as power dispatch, traffic control, and weather forecasting. The pattern of seasonality and trends in long time series are often complex, especially when they are presented at different time scales. Existing methods typically focus on only one scale or randomly select scales, which leads to a significant loss of valuable information. Additionally, current methods often transform multi-channel data into a single-channel format, ignoring interactions and complex relationships between channels. The paper proposes MultiWaveNet, a novel long time series forecasting framework that addresses seasonality as well as trends separately. For the seasonal component, the framework uses multi-scale wavelet decomposition to generate subseries at multiple scales. A learnable optimization factor is introduced simultaneously to separate high-frequency components mixed in low-frequency series after wavelet decomposition. In order to reduce information redundancy and model complexity, the paper develops a wavelet domain sampling encoder that consists of just one Transformer encoder, ensuring effective modeling of long-term dependencies while maintaining feature extraction effectiveness. As for the trend component, unlike previous research, the weights of channels are adjusted based on their importance, allowing the more crucial channels to have a greater impact and thereby addressing the limitations of individual processing methods. The paper performs extensive experiments on nine standard datasets, demonstrating that MultiWaveNet is the most competitive method. • Learnable optimization factor eliminate high-frequency information mixed in the low-frequency subseries. • Developed a wavelet-domain sampling encoder incorporating only the Transformer Encoder. • An attention-based channel-aware module was designed. [ABSTRACT FROM AUTHOR]

The research on bipedal robots with limited foot area is gaining increasing attention. To tackle the challenge of dealing with unknown disturbances in the environment, the adjustment of footstep placement plays a vital role in maintaining stable motion during bipedal walking. This paper introduces an innovative approach based on the relationship between the Divergent Component of Motion (DCM) and footstep. It utilizes a DCM prediction model to optimize the optimal speed for recovering the foothold position. The goal is to enable quick and relevant footstep selection for bipedal robots, thereby facilitating the swift recovery of robot speed. The paper provides insights into the process of designing the desired DCM for achieving an optimal average walking speed without relying on predefined footstep sequences. By establishing a state equation between the DCM and footstep placement, this approach enables the prediction of multiple footstep positions within a fixed walking cycle, thereby facilitating the desired average motion speed. Extensive numerical simulations are conducted to compare the proposed method with various conventional footstep adjustment algorithms. The results emphasize our method's ability to converge more rapidly to the target speed, even with minor step adjustments. To validate the feasibility and robustness of the algorithm, we conduct experiments on the bipedal robot BHR-B2. These experiments further confirm the algorithm's effectiveness. Given its promising performance, this algorithm holds potential for applications in legged robots with point feet. • The adjustment of the footstep is crucial for the speed recovery of bipedal walking. • A multi-step prediction footsteps adjustment algorithm based on DCM is proposed. • The desired speed of motion is mathematically related to the target DCM state. • Three adjustment algorithms were compared through simulation. • Algorithms have been applied and effectively validated on a bipedal platform. [ABSTRACT FROM AUTHOR]

The users' click prediction holds significant commercial value in online purchase platforms. Currently, some session-based interest extraction models overlook the impact of temporal interval factors on the distribution pattern of behaviors within a session. To address the above issue, this paper designs a module named session-based time-aware gated recurrent units (ST-GRU) to integrate the temporal interval factors into session sequence. The ST-GRU module utilizes a memory decay function, enabling the simulation process to identify the users' memory changes within each session. Subsequently, to account for the interdependence between different sessions, this paper introduces bi-directional gated recurrent units (Bi-GRU) with an attention mechanism to learn interaction relations of user interests. Additionally, for high-dimensional potential features, this paper integrates above modules and proposes a method called time interval aware deep session interest network (TIAE-DSIN) model. Correspondingly, comparative experiments are carried out on three public datasets, and the obtained results indicate that the AUC of TIAE-DSIN model is superior to other session interest extraction models. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AV)s have become a trending topic nowadays since they have the potential to solve traffic problems, such as accidents and congestion. Although AV systems have greatly evolved, it still have their limitations. For example, Google reported that their AVs have been involved in several collisions and near misses. While most of these collisions and near misses were caused by third parties, the AVs should be able to predict and avoid them. Events like this show that there is still room for improvement in the AV system. This paper aims to present a review of the state-of-the-art algorithms proposed to enable AV behaviour prediction systems to predict trajectories and intentions for pedestrians and vehicles. This will be achieved by using information from previous literature review papers, recent works, and results obtained using well-known datasets. • An AV behaviour prediction system must have a long (> 3s) prediction horizon. • It is a challenging task to model interaction between agents. • Datasets from onboard sensors should be used to predict road agents' behaviour. • Deep learning techniques have shown better results to predict behaviours. • Algorithms should be evaluated using the same datasets and evaluation metrics. [ABSTRACT FROM AUTHOR]

Grey prediction evolutionary algorithm (GPE) is an emerging category of meta-heuristic algorithm based on mathematical model. It generates offspring by predicting the macroscopic evolutionary trend of population series. On the contrary, the mining of local evolutionary trend are flawed. To address this imperfection, this paper introduces a quadratic function as a surrogate model to mine the local evolutionary trend of population series for the first time. Thereby an improved grey prediction evolutionary algorithm called grey prediction evolutionary algorithm with a surrogate model based on quadratic interpolation (GPE-QI) is designed. The surrogate model based on quadratic interpolation is regarded as an external auxiliary tool to mine the local evolutionary trend of population series by using currently superior individuals. Through utilizing the mined local trend information, a donor population closer to the optimal solution is formed to improve the prediction direction of the algorithm. The performance of GPE-QI is tested on CEC2019, CEC2020 benchmark functions and six engineering design problems. The experimental results demonstrate the stronger competitiveness of GPE-QI to well-established algorithms in terms of solving accuracy and convergence rate. If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE. • Improvement can be made by enhancing local search of grey prediction evolution. • Mine local evolutionary trend of population series by quadratic interpolation. • Construct a surrogate model by using the local evolutionary trend. • Design an improved grey prediction evolutionary algorithm with a surrogate model. • The proposed algorithm is more competitive than the well-established. [ABSTRACT FROM AUTHOR]

The accurate prediction of digging load serves as a fundamental cornerstone for advancing the development of intelligent and unmanned excavators. Given the complex nonlinear dynamics of digging load, this paper proposes a novel prediction model for excavator load power based on the chaos theory and support vector regression (SVR). The presence of chaos in the dynamic digging load system is detected through phase space reconstruction. SVR is utilized for nonparametric modeling and prediction, with the reconstructed phase space capturing the essential characteristics of excavator load and serving as inputs for SVR. To optimize the hyperparameters, an improved particle swarm optimization (IPSO) algorithm is presented. Excavation experiments conducted under two typical load conditions demonstrate the superiority of the proposed chaos-based IPSO-SVR model in terms of prediction accuracy. This research lays a solid foundation for practical load prediction in industrial excavator settings. [ABSTRACT FROM AUTHOR]

• We propose a novel hybrid model namely MEEMD-Mix-KELM to forecast crude oil price. • The emerging MEEMD, with mode-splitting prevention, is used to decompose oil price. • We create a new mix-kernel for ELM by absorbing merits of local and global kernels. • Empirical results show that the proposed hybrid model achieves significant effects. It is of vital importance for governments, enterprises, and investors to forecast crude oil prices accurately, while this task is beset with difficulties and challenges due to the complex patterns in oil prices. This paper aims to propose a novel hybrid method to model and forecast the crude oil price by integrating median ensemble empirical mode decomposition (MEEMD) and mix-kernel extreme learning machine (Mix-KELM). Firstly, the emerging MEEMD is employed to decompose the crude oil price into several simple subseries. Secondly, a novel mix-kernel is developed for extreme learning machine (ELM) by combining the advantage of the local kernel (i.e., Radial Basis Function in learning ability) and global kernel (i.e., Sigmoid in generalization ability), with weights of the kernels optimized through genetic algorithm. Thirdly, the proposed Mix-KELM is applied to forecast the subseries of crude oil price, and the sub-forecasting results are integrated to generate the final results. The empirical results show that our proposed MEEMD-Mix-KELM model with different forecasting horizons significantly outperforms the benchmarks in terms of forecasting accuracy and robustness test. Taking one-step-ahead forecasting as an example, the proposed model exhibits the lowest prediction errors in terms of mean absolute error, symmetric mean absolute percentage error, and root mean squared error with values of 1.1767, 0.0135, and 1.5717, respectively. [ABSTRACT FROM AUTHOR]

The uncertainty associated with option price predictions has largely been overlooked in the literature. This paper aims to fill this gap by quantifying such uncertainty using conformal prediction. Conformal prediction is a model-agnostic procedure that constructs prediction intervals, ensuring valid coverage in finite samples without relying on distributional assumptions. Through the simulation of synthetic option prices, we find that conformal prediction generates prediction intervals for gradient boosting machines with an empirical coverage close to the nominal level. Conversely, non-conformal prediction intervals exhibit empirical coverage levels that fall short of the nominal target. In other words, they fail to contain the actual option price more frequently than expected for a given coverage level. As anticipated, we also observe a decrease in the width of prediction intervals as the size of the training data increases. However, we uncover significant variations in the width of these intervals across different options. Specifically, out-of-the-money options and those with a short time-to-maturity exhibit relatively wider prediction intervals. Then, we perform an empirical study using American call and put options on individual stocks. We find that the empirical results replicate those obtained in the simulation experiment. • Conformal prediction quantifies well the uncertainty of option price predictions. • Conformal prediction intervals have empirical coverage near the nominal level. • Non-conformal prediction intervals have empirical coverage below the nominal level. • Large variations in prediction intervals are found for American call and put options. • Larger intervals are found for out-of-the-money and short time-to-maturity options. [ABSTRACT FROM AUTHOR]

The ongoing transition in the electricity market, coupled with the rising penetration of renewable energy sources, has spurred demand for enhanced load forecasting accuracy. Traditional single predictors exhibit limited robustness and diversity, rendering them unsuitable for most forecasting situations. Moreover, weather conditions, holidays, and social events significantly influence load demand, making forecasting techniques that account for these factors increasingly relevant. To bridge the gaps and further enhance predictive performance, in this paper, a novel adaptive multivariate combined forecasting system is proposed, incorporating redundant feature elimination, adaptive model selection, multi-objective intelligent optimization with non-negative constraints, and reliable interval prediction theory. The proposed system employs non-linear feature selection to identify effective load features and develops an advanced Multi-Objective Optimization Strategy (MOS) for combining multiple optimal predictors under various scenarios. The weight coefficients of sub-predictors are theoretically demonstrated to be Pareto optimal, and the MOS exhibits superior distribution and convergence. Simulation results reveal that the developed system substantially outperforms benchmark models, achieving reliable uncertainty quantification, which facilitates the rational allocation of spinning reserves and enhances dispatching efficiency. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) presents a decentralized approach to model training in the agri-food sector and offers the potential for improved machine learning performance, while ensuring the safety and privacy of individual farms or data silos. However, the conventional FL approach has two major limitations. First, the heterogeneous data on individual silos can cause the global model to perform well for some clients but not all, as the update direction on some clients may hinder others after they are aggregated. Second, it is lacking with respect to the efficiency perspective concerning communication costs during FL and large model sizes. This paper proposes a new technical solution that utilizes network pruning on client models and aggregates the pruned models. This method enables local models to be tailored to their respective data distribution and mitigate the data heterogeneity present in agri-food data. Moreover, it allows for more compact models that consume less data during transmission. We experiment with a soybean yield forecasting dataset and find that this approach can improve inference performance by 15.5% to 20% compared to FedAvg, while reducing local model sizes by up to 84% and the data volume communicated between the clients and the server by 57.1% to 64.7%. Our method demonstrates the potential to use efficient models that are more environmentally friendly to support the agri-food sector's transition to net zero. Future enhancements of this method could further optimize distributed learning in agri-food, enhancing sustainability and applicability. • We propose a new federated pruning learning method for yield forecasting. • Our method can be used to improve local inference performance. • The method reduces communication costs during training and model sizes. • Our method facilitates the on-edge implementation of ML models using sparse tensor. • The method is generalizable and works with different pruning policies and schedules. [ABSTRACT FROM AUTHOR]

This paper proposes a new model for creating fuzzy time series and conducting future forecasts using the Bi-directional Long Short-term Memory model (Bi-LSTM). This study contributes by using both classical machine learning and deep learning techniques. Firstly, a self-updating clustering algorithm is used to determine the optimal number of clusters for time series. Secondly, the study describes a method for determining the fuzzy relationship between elements of time series and the identified clusters. Thirdly, the developed model creates the fuzzy time series based on the established rules. Finally, forecasting for the future value using the Bi-LSTM model with some improvement in optimizing the parameters and increasing the accuracy of forecasting result. The steps of the proposed model are clearly illustrated by a numerical example. Moreover, this model also tested on various datasets, including M3, M4, and M5 datasets. By evaluating its performance using parameters such as sMAPE and RMSSE, the proposed model has shown significant enhancements when compared to existing models. Additionally, the model's effectiveness is further demonstrated through its successful application in forecasting the VN index stock in Vietnam. [ABSTRACT FROM AUTHOR]

The focus of this paper is to improve short-term load forecasting for electric power. To achieve this goal, the study explores and evaluates hybrid models, specifically using the CatBoost and XGBoost algorithms, which are optimized with different optimizers. The study incorporates hourly electricity load data and also includes temperature data to enhance the precision of the forecasting models. Statistical metrics are then used to assess the performance of these models. The study evaluates the performance of the hybrid models on both training and testing datasets. It finds that the CatBoost-Arithmetic Optimization Algorithm hybrid model outperforms the other models in the training dataset. However, in the testing dataset, the XGBoost- Arithmetic Optimization Algorithm hybrid model demonstrates superior performance compared to the CatBoost models. The study conducts an importance and sensitivity analysis to understand which variables have the most significant impact on the target variable, which is likely electricity load. The results of this analysis reveal that temperature is the most influential variable affecting the target variable. Additionally, the month variable is identified as having a notable impact on load forecasting. These findings suggest that employing hybrid models, particularly those optimized with appropriate algorithms, can significantly improve the accuracy of short-term load forecasting. Moreover, the study highlights the importance of incorporating temperature data into these models, as temperature is a key driver of electricity load patterns. [ABSTRACT FROM AUTHOR]

Telemonitoring of Parkinson's disease has important implications for early diagnosis and treatment of patients. Most of the existing feature selection methods for remote prediction of PD severity are based on correlation and rarely consider causality, thus compromising the robustness of the model. Therefore, a causal game-based feature selection (CGFS) model is proposed for remote PD symptom severity assessment. Firstly, to address the challenge of small data size, the similar patient transfer strategy is designed to find data from source domain patients with conditions similar to those of the target patient. Secondly, the undirected equivalent greedy search method is employed to construct the causal graph between features and PD severity scores, and the robustness of the model is improved by selecting causal features. Then, to enhance the prediction accuracy, this paper utilizes the cooperative game approach Shapley value to evaluate the contribution of neighborhood nodes of the target value, and selects the features with causality and high contribution to form the final feature subset. Finally, the subset is input into the random forest to further enhance robustness and performance of the model. Experiments on Parkinson's telemonitoring dataset and the tapping dataset with different biomarkers show that the robustness of the feature subset selected by the CGFS model, and the prediction performance is better than advanced models compared. Therefore, the validity and universality of the CGFS method is demonstrated in remote PD severity prediction. [ABSTRACT FROM AUTHOR]

Enhancing the accuracy of breast cancer recurrence prediction is crucial, mainly when dealing with genomics data, which presents challenges such as high dimensionality, noise, non-linearity, and limited sample sizes. This paper introduces Semi-Supervised Survival Laplacian Regression (S3LR), a novel feature selection algorithm designed to improve breast cancer recurrence prediction by effectively handling censored, event, and unlabeled data. S3LR modifies the Laplacian Score (LS) by incorporating a distance matrix to calculate the weight matrix, and it integrates heuristic and metaheuristic optimization algorithms to optimize the weighted matrix. These enhancements refine feature selection and overall performance. In our evaluations using three datasets and comparisons with state-of-the-art techniques, S3LR combined with Particle Swarm Optimization (PSO) demonstrates significant improvements in C-index and mean absolute error (MAE). Average C-index values reach 68.80 %, 59.49 %, and 67.66 %, with average MAE values of 15.98, 7.87, and 8.65 months, respectively. These results showcase S3LR's effectiveness in predicting recurrence, even with censored data, for more precise and reliable outcomes. Furthermore, the framework's versatility extends beyond breast cancer and can readily be applied to address other survival and recurrence problems. [ABSTRACT FROM AUTHOR]

In the context of today's energy shortage, wind energy plays a crucial role as one of the most widely used renewable energy sources. However, in order to fully utilize the potential of wind energy, it is necessary to build a precise and reliable wind-speed prediction system. Conventional wind-speed prediction models frequently rely on a solitary model for forecasting, and sometimes these models cannot accurately fit the nonlinear characteristics of the data, resulting in poor prediction performance. Therefore, to address this deficiency, a new wind-speed point-interval prediction system is proposed in this study. The fuzzy information granulation technology is adopted in this system to reduce the dimension of data and solve the problem of redundancy in wind-speed data. In addition, this paper uses the multi-objective dragonfly algorithm and combines sub-models using the multi-nonlinear weight strategy to address the issue of inadequate precision exhibited by an individual prediction model. In order to verify the effectiveness of this wind-speed prediction system, the Penglai wind farm in Shandong Province is used as the dataset to construct the model, and several reference models are selected to conduct point prediction and interval prediction comparison experiments respectively. The experimental results show that the combined prediction system has the best prediction performance, with MAPE values of 14.16%, 15.02% and 15.74%, respectively. Compared with other benchmark models, the average improvement rates for the multi-step prediction were 14.97%, 16.48% and 19.11%, respectively. [ABSTRACT FROM AUTHOR]

Predicting microbe–disease associations (MDA) is crucial for proactively demystifying diseases causes and preventing them. Traditional prediction methods endure labor-intensive, time-consuming, and expensive. Therefore, this paper proposes CasMF-GCL, a novel G raph C ontrastive L earning model based on sparse relation augmentation and a Cas caded M ulti-kernel F usion Network for MDA prediction. CasMF-GCL maximizes the use of sparse correlation information and eliminates data noise through an association augmentation technique based on low-rank sparse matrix completion. We employ diverse strategies to enhance the features of heterogeneous networks by exploring the similarity between microbes and diseases. Our model employs a new multi-layer graph convolutional network variant with a cascaded multi-kernel fusion mechanism, enabling weighted coding of local and global features for diseases and microbes. To further improve performance, we incorporate a self-supervised contrastive learning schema using multi-grain disease and microbe features and large-scale relation augmentations. Extensive experiments on two renowned datasets demonstrate that CasMF-GCL outperforms current state-of-the-art methods in seven indexes. The AUC values from 5-fold cross-validation are 0.997 and 0.989 for the two datasets, respectively. Ablation studies confirm the effectiveness of graph data augmentation, the power of the contrastive strategy, and the indispensability of the cascaded multi-kernel fusion network. Furthermore, case studies validate the prediction performance of CasMF-GCL. • Proposing a novel MDA prediction model called CasMF-GCL. • Designing a method for augmentation MDA graph data. • Exploiting a novel multi-kernel similarity fusion mechanism. • Integrating contrastive learning to the MDA task. • Learning the most expressive features to training an efficient and robust model. [ABSTRACT FROM AUTHOR]

A new model of utility analysis and performance prediction in crowdfunding is proposed in this paper with considering backer's behavior-related decision. To establish a connection between the backer's behavior-related decision and crowdfunding performance, the proposed model includes a modeling analysis and an empirical analysis. The modeling analysis focuses on analyzing backer's backing decision with establishing the utility-driven model, while the empirical analysis verifies the association between the utility-related features and crowdfunding performance, as well as the prediction improvement of the proposed model. Specifically, prospect theory (PT) is applied first in the modeling analysis part to analyze backer's prospect utilities, and then evidential theory (ET) is applied to aggregate the multi-source dynamic feature's utility. To validate the proposed model, and based on the results of modeling analysis, we conduct the significant test with regression model and the prediction test with several machine-learning prediction model to verify whether the proposed utility features can improve the prediction results in terms of crowdfunding performance. The empirical results show that the correlation between behavior-related factors and crowdfunding performance is significant. And the area under the curve (AUC) index of the proposed model is, on average, 7.39% higher than the baseline model on several machine-learning prediction models. In addition to providing new insights for predicting crowdfunding results, this research provides a powerful tool to assist crowdfunding platform and fundraisers in operational management with analyzing backers' backing behavior. • Modeling backers' behavior-related decisions in crowdfunding. • Empirical results validate the effect of behavior-related features on performance. • On average, the proposed model improves prediction accuracy by 7.39%. [ABSTRACT FROM AUTHOR]

Forecasting wind power is crucial to ensuring the electricity system runs steadily. However, wind power forecasting is hampered by the randomness and volatility of wind power data. In this paper, a deterministic and probabilistic decomposition ensemble model based on improved feature screening and optimal Gaussian mixed kernel function is proposed, which mainly includes feature screening, feature learning, intelligent weighted integration and interval prediction modules. First, an improved feature screening (IFS) is proposed to capture the optimal features of the original wind power data, eliminating the interference of redundant features. Then an optimized deep neural network is suggested to learn the outcomes of IFS and further explore the temporal correlation of these features. Then, considering the difference of the contribution of each feature component, the final result is obtained by integrating the preliminary forecast results using intelligent weighted integration (IWI). Finally, an optimal mixed kernel-based Gaussian process regression model is developed for interval prediction, which improves the adaptability of the model. The data sets of two wind farms in Inner Mongolia were used for empirical testing. On the two datasets, the model's root mean square error (RMSE) is 1.1845 and 1.7354, respectively, both of which are lower than the comparison model. The model has the lowest coverage width criterion (CWC) of 0.1499 and 0.1460 on the two datasets, respectively. [ABSTRACT FROM AUTHOR]

Referring expression comprehension (REC) requires locating the region referred by the expression, where one of the key challenges is to distinguish the correct object from other of the same category using the described relationships. Existing two-stage methods explicitly establish the visual relationships among objects based on spatial information, including the location and scale. This paper investigates the role of relational features. We find that the predicted result becomes incorrect when the region scale changes. The trained model statistically tends to predict larger regions as the results and performs worse for objects of smaller scales. To alleviate this problem, we propose a Scale-Insensitive Network (SINet) to improve the robustness to scale information during the visual relational feature modeling process. Specifically, a category-wise random pooling module is designed to efficiently change object scales, and SINet simultaneously takes the original and resized regions as inputs. We introduce a consistency loss to train the model to remain correct under different scales. Our method can be integrated to existing two-stage methods for alleviating the dependence on scale information and promoting their utilization of key visual features. Extensive experimental results on 3 commonly used datasets, including RefCOCO, RefCOCO+ and RefCOCOg, have demonstrated the superiority of SINet to the state-of-the-art two-stage methods in terms of REC accuracy. • We are the first to consider the effect of region scale on the REC performance. • The proposed scale-insensitive network can be integrated into any two-stage methods. • We have conducted extensive qualitative and quantitative experiments on widely used datasets. [ABSTRACT FROM AUTHOR]

To simultaneously predict tool wear and surface roughness accurately during machining, this paper develops a multi-target predictive model that leverages a novel two-step feature-integration approach and Multi-kernel Gaussian process autoregressive regression (MK-GPAR). Compared with the traditional Gaussian process autoregressive regression (GPAR) models, the MK-GPAR demonstrates an enhanced capability in capturing detailed structures within heterogeneous data originating from various sources or disparate data forms. The weight coefficients of the kernel functions in MK-GPAR are typically assigned a value of 1 to minimize their influence on the predictive model. Furthermore, when compared to conventional multi-target predictive models like Gradient Boosting, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN), the GPAR not only delivers higher-accuracy predictions for tool wear and surface roughness but also provides corresponding confidence intervals (CI). However, the CI of predictions for tool wear and surface roughness provided by MK-GPAR are inconsistent, which will seriously affect the reliability of the evaluation of the prediction results. The proposed two-step feature-integration approach is employed to enhance signal features effectively, reduce noise, and mitigate its adverse impact, thus enhancing the consistency and smoothness of the CI. The two-step feature-integration approach integrates principal component analysis (PCA) and orthogonal neighborhood preserving projections (ONPP) to merge force and vibration signal features obtained from the fixture's monitoring signals. Following the two-step feature-integration (first PCA, then ONPP) process, the MK-GPAR is utilized to build a multi-target predictive model for tool wear and surface roughness prediction. The impact of the proposed two-step feature-integration method on the prediction performance of the MK-GPAR is demonstrated revealed milling experiments. The experimental findings indicate that the proposed two-step feature-integration method significantly enhances the accuracy of predictions and the consistency of CI of the MK-GPAR. This research lays the foundation for implementing the multi-target predictive model in real industrial environments to predict tool wear and surface roughness. [ABSTRACT FROM AUTHOR]

In single-well production prediction tasks, the achieved forecasting performance is sensitive to various uncertain factors. Most existing methods are either based on trend lines of the production history or overall development process data. However, the linkages between diverse data types and models have largely not been considered. In this paper, we construct a novel stacking model to fuse the static geological information, dynamic well production history, and spatial information of adjacent water injection wells. This stacking model consists of three basic modules that serve as encoders to separately extract features from these three types of information. The first is a multilayer perceptron (MLP), which analyzes the static geological properties of the reservoir that might influence the well production rate. The second is a long short-term memory (LSTM) module, which extracts the temporal information that is reflected in dynamic production variations. The third is also an LSTM module, as a spatial information processing module, it considers the time delay of the water flooding response from the injection well to the target well. The information fusion quality of the stacking model is validated from the perspective of causality discovery using symbolic transfer entropy (STE). Experiments are conducted on a block dataset derived from a water flooding oilfield. The results show that the presented model can make full use of its model structure and greatly improve the accuracy and generalizability of prediction. • Constructing a new stacking model for single well production prediction. • Our model can fuse three types of information influencing single well production. • Validating the information fusion quality of the stacking model. • Experiments show that our model achieves good performance on a block data set. [ABSTRACT FROM AUTHOR]

Multi-head selection is a reasonable way of extracting relational facts. Though effective, it ignores the interdependencies of relations and disregards the contextual information. In this paper, we propose a relation guided and attention enhanced approach to address the above challenges. Specifically, we predict the relations existing in the input sentence to guide multi-head selection. This strategy helps to model dependencies of relations. Moreover, we use an attention mechanism to leverage the sentential context. The experimental results demonstrate that our approach significantly outperforms the baselines. [Display omitted] • Multi-head ignores the interdependencies of relations and the contextual information. • A novel RP auxiliary task to guide main table-filling task by pre-predicted relations. • The RP layers can also strengthen the complex interdependencies of relations. • The Token-Pair attention mechanism can leverage the sentential context. [ABSTRACT FROM AUTHOR]

Mesoscale Eddy (ME) is a widely recognized and significant oceanic phenomenon characterized by extensive energy exchange. Accurate predictions of the future geospatial distribution of ME are crucial for maritime activities. Deep learning (DL) methods for ME prediction have consistently outperformed classical mathematical models and numerical modeling approaches. However, current DL methods based on two-dimensional gridded data suffer from error accumulation issues due to their indirect prediction approach, which involves first predicting the future environmental field and then identifying the ME patterns within it. Additionally, these models ignore the potential influence of prior knowledge related to ME on prediction outcomes. To address these issues, this paper proposes a direct prediction DL network called ES-ConvGRU, which incorporates historical prior statistics. First, we conduct an analysis of ME data from the Northwest Pacific region, extracting prior statistical knowledge such as interannual variations and seasonal characteristics. Second, mining patterns for the spatiotemporal features of both ME sequence and sea surface environmental field sequence are established. Furthermore, a nonlinear approach for mapping prior statistical knowledge and a multi-step autoregressive prediction model are designed, facilitating the integration of prior knowledge with multidimensional spatiotemporal features for ME prediction. Finally, the performance and effectiveness of ES-ConvGRU are evaluated. It achieves an average pixel accuracy of 93.21%, a mean intersection over union of 81.48%, and a frequency-weighted intersection over union of 87.55% for the 7-day distribution prediction. The results show that ES-ConvGRU exhibits notable advantages compared to the existing indirect and direct prediction methods. • A direct predictive approach for mesoscale eddy distribution prediction is developed. • Prior seasonal statistics is considered for prediction. • An end-to-end deep prediction network architecture, ES-ConvGRU, is proposed. • The results show that ES-ConvGRU performs better than other predictive methods. [ABSTRACT FROM AUTHOR]

With the continuous evolution and refinement of urban functional spaces, the escalating reliance of commuters on public transportation for work-related travel has surged with time. This paper offers a multidimensional overview of research pertaining to identifying, analyzing, and forecasting of urban public transportation commuting patterns. Initially, emphasis is placed on the identification of commuting patterns. We delineate the characteristics of four data sources, summarize the establishing of travel chains, and classify the approaches involved in identifying commuting patterns. Subsequent to this, we delve into the type of determinants influencing commuters, exploring the ramifications of spatial–temporal heterogeneities. Thereafter, we classify and expound upon forecasting methodologies for commuter flow, elucidating principles behind predictive algorithms and composite models grounded in statistical and time-series analyses, discrete choice modeling, conventional machine learning, and advanced deep learning techniques. Concurrently, a comparative assessment of the strengths, limitations, and applicability of each method is presented. Concluding our exposition, predicated on current research landscapes and inherent challenges, prospective trajectories for future exploration are proposed, including multi-source data fusion, real-time commuting pattern detection, precise public transit for commuting, and commuting travel under shared transportation. [ABSTRACT FROM AUTHOR]

Surface roughness is a critical parameter for quantifying the surface quality of a workpiece. Accurate surface roughness prediction can significantly influence the overall quality of the final components by reducing costs and enhancing productivity. Although various prediction methods have been explored in previous research, less attention has been given to explainable prediction methods for surface roughness. This paper proposes a surface roughness prediction model based on the Differential Evolution (DE) algorithm with ensemble learning. It aims to elucidate the intrinsic correlation between processing parameters and surface roughness values in Multi-Jet Polishing (MJP) using explainable analysis methods. The proposed Ensemble Regression with Differential Evolution (ERDE) algorithm comprises two main modules: data processing and analytics, and ensemble model prediction. These modules investigate the surface roughness of MJP from the data and model levels, respectively. To validate the effectiveness of ERDE, MJP experiments were conducted on three-dimensional printed components of 316L stainless steel. The experimental data indicated that ERDE outperforms other existing algorithms, reducing the mean absolute percentage error by approximately 42 %. [ABSTRACT FROM AUTHOR]

To enhance RUL prediction accuracy and uncertainty quantification, numerous methods have been developed, including model-based, data-driven, and hybrid approaches. However, model-based approaches struggle with complex relationships and uncertainties. Data-driven methods might overlook prior knowledge and struggle with limited data. Hybrid models for RUL prediction face two key challenges: inadequate use of physical information and difficulty in accurately quantifying uncertainty. Aiming at the problems of non-linearity, small sample sizes, and the absence of uncertainty quantification in RUL prediction, this paper introduces a hybrid method aimed at achieving RUL prediction and uncertainty quantification in few-slot scenarios. In this study, a hybrid approach that combines model-based approach and data-driven approach is proposed to achieve accurate RUL prediction. The uncertainty is measured based on a Bayesian framework. Specifically, the proposed method combines the degradation trend model using a double-exponential degradation model (DEDM), and the degradation fluctuations is predicted by a Gated Recurrent Unit (GRU) network to achieve point estimation of the RUL. Subsequently, an ensemble learning method is adopted to integrate the different modules using a Bayesian neural network (BNN) for uncertainty quantification. The applicability and effectiveness of the proposed method is investigated through case studies conducted on the three lithium battery datasets. The comparative analyses are also conducted with commonly used EMD-based approaches. Experimental results demonstrate that the proposed method has good performance in both data leakage and non-leakage scenarios and is more effective than individual methods to achieve accurate RUL prediction under small sample datasets. Finally, this study dedicates to integrate physical models and data-driven models to address the challenge of data drift in future research. [ABSTRACT FROM AUTHOR]

Accurately forecasting demand is a potential competitive advantage, especially when dealing with perishable products. The multi-billion dollar horticultural industry is highly affected by perishability, but has received limited attention in forecasting research. In this paper, we analyze the applicability of general compared to dataset-specific predictors, as well as the influence of external information and online model update schemes. We employ a heterogeneous set of horticultural data, three classical, and twelve machine learning-based forecasting approaches. Our results show a superiority of multivariate machine learning methods, in particular the ensemble learner XGBoost. These advantages highlight the importance of external factors, with the feature set containing statistical, calendrical, and weather-related features leading to the most robust performance. We further observe that a general model is unable to capture the heterogeneity of the data and is outperformed by dataset-specific predictors. Moreover, frequent model updates have a negligible impact on forecasting quality, allowing long-term forecasting without significant performance degradation. • A comprehensive study on time series forecasting for horticultural sales. • Machine learning outperforms classical forecasting in horticultural sales prediction. • Statistical and calendar features are beneficial for horticultural sales prediction. • Detailed analysis of the impact of model refitting schemes on forecasting quality. • Company-specific forecasting models are superior to cross-company predictors. [ABSTRACT FROM AUTHOR]

Nowadays information technology advances allow the collecting and storage of large complex datasets in many areas. Modeling and forecasting interval-valued time series (ITS) has drawn much attention over the last two decades because interval-valued observations contain more information than point-valued observations over the same period and remove undesirable noises in high-frequency data. However, most work mainly focuses on modeling a linear univariate ITS or bivariate point process. This paper proposes nonparametric regression models for interval-valued time series with imposing constraints, e.g., monotonicity. This setting with a monotonic constraint is consistent with the existing literature, which focuses on incorporating valuable empirical information in modeling and forecasts. Two constraint estimators are developed and asymptotic properties are established. Monte Carlo simulation is conducted to show the finite sample performance. An empirical application to equity premium documents that the proposed model yields a better forecast performance than some popular models in the literature. • First applies the parsimonious nonparametric spirit to interval time series. • Provide an algorithm obtaining the constraint interval estimators with bagging. • Derive the consistency and limit distribution of the proposed estimators. • Outperforms existing methods in the interval predictive model for stock returns. [ABSTRACT FROM AUTHOR]

In steel enterprises, the long-term interval prediction of downstream gas demand plays an increasingly significant role in the scheduling decision-making of gas supply networks. Accurate long-term interval prediction remains a challenge owing to the intermittent and fluctuating characteristics of ironmaking and steelmaking. In this paper, a novel granulation-based interval prediction model (STGLUBE) is developed, which considers various data granule features and spatial–temporal correlated features. Granular evaluation indices and a corresponding training method are designed for STGLUBE training. A hybrid long-term interval prediction method integrated with a multiscale decomposition and error correction mechanism is further proposed to satisfy the actual requirements. In this method, an empirical wavelet transform is used to extract simplified modes from the original gas demand. The proposed STGLUBE is implemented in the primary mode, whereas the point predictions are performed in other modes. In addition, an error correction mechanism is introduced to enhance prediction accuracy. The effectiveness of the proposed method is verified by conducting two industrial case studies, and the results illustrate that the proposed method outperforms the existing widely used methods. [ABSTRACT FROM AUTHOR]

Over the past few years, data-driven video compression has achieved encouraging results and attracted increasing attention. However, previous works rely on feature space operations and residual coding, where feature space operations may degrade reconstructed frame quality due to estimated offset maps overflow, and residual coding uses a simple subtraction operation to remove redundancy that cannot explore the temporal correlation effectively. Also, error propagation is a common problem owing to the accumulation of reconstruction errors in inter-frame coding. To solve these issues, we propose an efficient video compression scheme in this paper. First, we propose a flow-guided feature prediction module to directly use optical flow for explicit guidance in the feature space. Second, we propose a temporal context compression module to replace the residual coding, which explores the hierarchical prior and temporal prior information and fuses them to boost the coding performance. Third, we further propose a three-stage training strategy to take advantage of single-frame and multi-frame information to avoid error propagation. Comprehensive experiments demonstrate that our proposed method achieves higher coding performance than the existing learning-based schemes and surpasses the current advanced coding standard (H.266/VVC) with low latency P frame configuration in MS-SSIM metric. • Flow-guided feature prediction guides the offset maps learning explicitly. • Temporal context compression uses conditional coding for better performance. • Three-stage training strategy avoids error accumulation. • Our proposed method outperforms the H.266(VTM) in MS-SSIM metric. [ABSTRACT FROM AUTHOR]

News recommendation (NR) is critical for helping users to navigate the vast amount of information on online news platforms. However, the key challenges of tackling the cold-start problem, comprehensively modeling user interests and accurately encoding news semantics in NR have not yet been effectively addressed by existing methods. In this paper, we propose to alleviate the cold-start problem by exploiting news popularity. We also argue that the browsing history of a user can be organized as a personalized hypergraph to augment user interest modeling. Furthermore, we suggest enriching the representation of a candidate news article by retrieving its similar news. Our proposed NR method, named P opularity P rediction with S emantic R etrieval (PPSR), features a retrieval-driven popularity predictor that leverages click records to predict news popularity by incorporating semantic retrieval. It also comprises a hypergraph-based user encoder that mines the rich and diverse relatedness among news in a user's browsing history. In addition, it designs a semantic-enhanced news encoder that retrieves and utilizes similar news in the training set to enrich semantic encoding for candidate news. Experiments on real-world datasets show that our PPSR can outperform the state-of-the-art methods in terms of higher recommendation accuracy and can well mitigate both the user-level and news-level cold-start problems. [ABSTRACT FROM AUTHOR]

The metro system is indispensable for alleviating traffic congestion in the urban transportation system. Precise metro passenger flow (MPF) prediction is crucial in ensuring smooth operations of the metro system. Recently, the graph convolutional network (GCN), which is effective in the spatial feature extraction, has been applied in traffic prediction. However, most existing GCN-based methods construct the empirical graphs based on distance and adjacency, which cannot fully express the correlations of metro stations. This paper proposes a novel MPF prediction method consisting of three parts: K-means-based metro station functional clustering (KMSFC), external feature fusion, and dual-view recurrent GCN (DVRGCN). The KMSFC identifies the metro stations both having similar MPF changing tendencies and being located in similar urban functional areas. Furthermore, the DVRGCN is designed to simultaneously capture the spatiotemporal and external features. The dual-view GCN module in the DVRGCN captures both explicit and implicit spatial features of the metro traffic network. To demonstrate the capability for making accurate MPF predictions, the experiments using a real-world metro traffic dataset are conducted. The ablation experiments are also performed to prove the contribution of each module in the proposed method. The experimental results show that the proposed method outperforms other state-of-the-art traffic prediction methods. • Precise clustering of passenger travel patterns at different metro stations. • Comprehensive consideration of spatial and temporal features of metro networks. • Informative presentation of passenger flow volumes for different metro stations. • Accurate predictions of future passenger flows of all metro stations. [ABSTRACT FROM AUTHOR]

Emergency Department overcrowding is a well-known problem. The consequences are long waiting times for patients, reduced service quality, and the potential for increased mortality rates. Moreover, this problem is escalating with the aging of the population and the chronification of diseases. Being aware of such concerns, Emergency Departments started to conduct patient accountability years ago, and now they are ready to take advantage of the gathered data to improve the quality of service to citizens. However, to achieve this goal, they need the technology that enables the generation of predictive models from such data to improve the planning of their resources. In this paper, we face the task of forecasting patient admissions by using Deep Neural Networks, particularly a modern Attention-based model. As well, historical records do not provide a full picture of how patients attend the emergency service. Relying on the possibilities given by the attention mechanism, we also propose the use of exogenous information such as calendar data, weather, air quality, allergens, and information extracted from the web via Google Trends to enhance admissions prediction accuracy. In this regard, we have tested different configurations of the exogenous variables to isolate which ones provide relevant information that improves the model. In our experiments, we have seen that the calendar, weather, and air quality provide the most valuable information, meanwhile, allergens and Google Trends data appear to be hindering the models' performance. • Using Attention model for forecasting Emergency Department (ED) patient admissions. • Comparing Attention model with LSTM-based networks for ED patients admissions. • Exogenous variables: weather, calendar, air quality, allergens and Google Trends. • Analyzing the impact of each type of exogenous variable in the forecast. • Attention model with calendar data achieves the best results. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) power forecast is crucial for carbon neutrality. Current researches on PV power forecast mainly focus on using temporal information from single PV station, and the spatial information in multiple PV power stations are often neglected. To address this problem, this paper introduces Moran index to verify the spatial autocorrelation of PV power for the first time, uses Granger causality test and transfer entropy to reveal the spatial information gain in PV power forecast for the first time, and proposes a novel spatial–temporal probabilistic PV forecast method using deep Graph Gated Recurrent Unit (GraphGRU) network-based spatial–temporal multi-task learning and Kernel Density Estimation (KDE). Deep GraphGRU combines the advantages of Graph Convolutional Network (GCN) in spatial feature extraction and the advantages of Gated Recurrent Unit (GRU) network in temporal feature extraction, and thus has strong ability to extract spatial–temporal information in historical data of multiple different PV power stations. Through GraphGRU, temporal dependency information extracted from historical data of multiple PV stations can promote each other to improve the forecast accuracy of each PV stations. KDE is used for estimating the joint probabilistic density function and giving the spatial–temporal probabilistic confidence interval of PV power. Experiments were performed in the five-year actual PV power data from 11 provinces of Belgium and the three-year solar irradiation data from 12 places in China to verify the superiorities of the proposed method. Comparison with conventional spatial–temporal and temporal forecast methods show that the proposed GraphGRU-based spatial–temporal forecast method can extract the spatial–temporal information from multiple PV power stations well and significantly outperform conventional temporal forecast methods. [ABSTRACT FROM AUTHOR]

Patient outcomes forecasting (POF) has been shown to be an effective diagnostic assistant that can be used to predict disease progression and patient status in advance. In practice, the variation in patient progress and physical symptoms poses significant challenges for POF model training and testing. These differences result in POF models trained on one set of patients (source domain) not being efficiently self-adapted to the disease prediction task of another set of patients (target domain). Domain adaptive approaches can balance the overall distribution differences, named long-term divergence between patient training data and test data. However, the complex time series dependence and multivariate distribution properties of patient clinical data lead to the inability of existing domain adaptation methods to guarantee consistent distribution of patient clinical data at a finer granularity, named short-term divergence, e.g., clinical data distribution at different time intervals. In this paper, we propose a novel POF approach (TADA) based on clinical time series data using T emporal-aware A dversarial D omain A daptation. In TADA, the traditional binary domain labels are generalized to act as the supervision signals to adversarially bridge the domain gap considering both long-term and short-term clinical information. TADA not only preserves the long-term representation of the clinical data but also captures the short-term data distribution at each period. The experimental results, based on both the publicly available and private clinical time series datasets, demonstrate that TADA can provide more reliable forecasting than state-of-the-art approaches. • Both long-term and short-term domain gap are simultaneously addressed. • A temporal-aware adversarial adaptation model to handle clinical time series data. • Theoretical analysis is provided for the patient outcomes forecasting model. [ABSTRACT FROM AUTHOR]

Accurately predicting support resistance in coal mine working faces is a pressing matter in the field of intelligent mining research, which holds immense importance in enhancing the safety of coal production and the overall level of intelligent mining. The current research in this field primarily focuses on utilizing deep temporal sequence models for prediction. While there have been promising advancements in this area, concerns persist regarding the dependability of predicted outcomes. In fact, the movement of overburden in mining faces varies over time and space, and the changes in the support resistance that reflect the instability of overburden movement also have temporal and spatial characteristics. Therefore, in order to improve the reliability of model prediction, this paper proposes the application of deep spatiotemporal sequence models for support resistance prediction, and identifies the PredRNN and PredRNN++ models as suitable options. The exploration on the application of deep spatiotemporal sequence models for predicting the support resistance has been conducted. The proposed application involves the establishment of a fully working face area grid model with support number and mining cycle as coordinate units. Different from deep temporal sequence models that take the studied grid features as observations, the 25 adjacent spatiotemporal feature grids corresponding to the studied grid are considered as observations for the deep spatiotemporal sequence models. Based on the predicted three-dimensional tensor, the required predicted support resistance data can be obtained. Based on the complete support resistance data from 9 working faces in a coal mine in Datong mining area from 2009 to 2018, a comprehensive dataset was established to experimentally analyze the predictive performance and reliability of the LSTM, PredRNN, and PredRNN++ models. The experimental results demonstrate significant improvements in predictive performance and reliability of deep spatiotemporal sequence models compared to the current mainstream deep temporal sequence models. In terms of predictive performance, the PredRNN and PredRNN++ models have improved their feature extraction ability by incorporating the convolution operations to extract the spatial correlation of the input data. Furthermore, through the incorporation of gated units and nonlinear neurons into the storage units, coupled with the integration of vertical memory state transitions, the depth of the network has significantly increased, resulting in the enhancement of the sequence modeling capabilities. In terms of reliability of prediction results: The acceptable threshold for prediction error was set at 1000 kN. According to the average prediction results of all samples in the test set, it was found that the deep temporal sequence model was unable to accurately predict support resistance ahead. While the deep spatiotemporal sequence model demonstrated a predictable sequence length of 8. According to the prediction results of all samples in the test set, the accurate prediction rate increased from 30 %∼35 % to 65 %∼85 % and the maximum MAE value decreased from 10000 ∼ 12500kN to 6500 ∼ 9000kN in more than 20,000 test samples. The PredRNN model with an input sequence length of 50 achieves an accurate prediction rate of 85.48 % and a maximum MAE value of 6698kN, making it the most reliable prediction model. [ABSTRACT FROM AUTHOR]

Deep fuzzy systems are widely used in time series forecasting tasks due to their excellent nonlinear transformation capabilities and interpretability. However, traditional optimization methods and fixed network structures cannot make the deep fuzzy system obtain the expected prediction accuracy and generalization ability. Therefore, a novel hybrid deep fuzzy model (HDFM) is proposed in this paper. Firstly, two types fuzzy modules, namely the type-1 TSK fuzzy module (T1TSKFM) and the interval type-2 TSK fuzzy module (IT2TSKFM), are respectively presented and designed in detail. And, the gradient expressions of the fuzzy parameters, including the antecedent and consequent parameters, are also derived in detail. Secondly, in order to optimize the fuzzy parameters and to further accelerate the module convergence, a novel parameter optimization strategy is presented, combining the gradient descent with the Regularization, the DropRule and the AdaBound algorithms. Thirdly, a new stacked hybrid deep fuzzy architecture is proposed, which can be automatically trained and constructed using the designed T1TSKFM and the IT2TSKFM. Then, the detailed data-driven learning and updating strategy are given in step by step way. In addition, both the layered structure interpretability and the fuzzy rule interpretability are respectively analyzed. This can guarantee that the proposed model not only has the better forecasting accuracy, but also has the higher interpretability. Finally, in order to verify the effectiveness of the proposed method, several comparative experiments are given. Experimental results show that the forecasting performance of the proposed model outperforms the other comparisons, such as the DIRM-DFM, the IT2DIRM-DFM, the DCFS, and the ANFIS method. At the same time, the proposed model has better interpretability and more flexible construction property. • Giving the gradient formulas of the interval type-2 TSK fuzzy module. • Presenting a novel gradient-based optimization algorithm. • Proposing a new stacked hybrid deep fuzzy architecture. • Giving the detailed data-driven learning strategy for the proposed model. • Analyzing the interpretability in detail. [ABSTRACT FROM AUTHOR]

The application of Machine learning and deep learning techniques for time series forecasting has gained significant attention in recent years. Numerous endeavors have been devoted to automating forecasting through the utilization of cutting-edge neural networks. Notably, the recurrent neural network (LSTM – Long Short-Term Memory) has emerged as a central concept in most research endeavors. Although LSTM was initially introduced in 1997 for sequence modeling, subsequent updates have primarily focused on language learning tasks. These updates have introduced various computational mechanisms within the LSTM cell, including the forget gate, input gate, and output gate. In this study, we investigate the impact of each computational component in isolation to analyze their effects on time series forecasting tasks. Our experiments utilize the Jena weather dataset and Appliance Energy Usage time series for evaluation. The experimental results reveal that variations of the LSTM model outperform the most popular LSRM cell format in terms of error rate and training time. Specifically, the variations identified in this paper demonstrate superior generalization capabilities and yield reduced forecasting errors. [ABSTRACT FROM AUTHOR]

Accurate forecasting of the agricultural power load is important for rural electricity networks' safe and stable operation. But it is more uncertain and difficult to forecast than industrial, commercial, and residential loads. Multivariate time series prediction methods have problems such as poor refinement of weather services and difficulty in quantifying the influencing factors. Univariate time series prediction methods should be emphasized, and the characteristic information in the historical load data should be fully exploited. Therefore, this paper systematically investigates the decomposition strategy and attention-based recurrent neural network for univariate time-series multi-step prediction. The decomposition strategy is employed to generate derived variable sequences from the target sequence as inputs for the model, while the dual-stage attention mechanism is used for dynamically selecting features. A one-year real-world dataset of agricultural power loads in southern China is used for validation. The effectiveness of two different decomposition algorithms on dual-stage attention-based recurrent neural network (DARNN) is discussed, then the proposed ensemble empirical mode decomposition and dual-stage attention-based recurrent neural network (EEMD-DARNN) is compared with four univariate time series prediction models and three ablation models. The proposed model is optimal in four evaluation indicators, with R 2 , R M S E , M A P E , and M A E of 0.970, 12.298, 0.040, and 7.335 respectively in the shortest forecast horizon (15 min), and 0.941, 17.305, 0.065 and 11.347 respectively in the longest forecast horizon (1 h). It is a significant improvement over the existing models. In conclusion, EEMD-DARNN can achieve more accurate ultra-short-term agricultural power load forecasting. [ABSTRACT FROM AUTHOR]

Self-supervised graph representation Learning (SSGRL) is an emerging technique for machine learning-based expert applications. SSGRL can effectively encode unlabeled data into machine-understandable knowledge embeddings that can be used in downstream expert tasks. Recently, Masked-Graph-Model (MGM) has achieved extraordinary performances in the field of SSGRL. However, MGM inevitably suffers from a so-called Negative-Migration (NM) problem on unbalanced datasets for specific tasks such as graph classification. The NM problem forces the MGM to predict (reconstruct) the dominant nodes causing the ignoring of the minority but critical nodes. To this end, a novel online prediction module is proposed to dynamically predict the nodes with extended contexts that enable minority nodes can have more weights in the model. Based on the online prediction module, a graph representation learning model is proposed to alleviate the NM problem. Extensive experiments demonstrate that the model outperforms state-of-the-art methods on unbalanced datasets and achieves competitive performance on balanced datasets. Moreover, extra experiments demonstrate that the proposed method requires less computational resource overhead than existing methods. The code is available at: https://github.com/DuanhaoranCC/SimGOP. • This paper explores the negative migration problem encountered in unbalanced datasets. • We propose online predict contextual node features, but the not the original data. • The results of graph classification show that the proposed method achieves SOTA results. [ABSTRACT FROM AUTHOR]

Accurate and reliable load forecasting can ensure the safety and economy of power system operation. To improve the accuracy of short-term power load forecasting, this paper adopts feature extraction combined with a variety of machine learning methods,(Empirical wavelet decomposition (EWT), Convolutional neural network (CNN) model, Recurrent neural network (RNN) model, Long short-term memory network (LSTM) model, Bayesian optimization (BOA) algorithm). A new hybrid forecasting model, the EWT-CNN-S-RNN + LSTM model is proposed to predict power consumption. In this method, the fixed mode of EWT decomposition is used to extract statistical features, and the LSTM/RNN model is selected according to the statistical features. This method uses the Bayesian optimization (BOA) algorithm to optimize the parameters to solve the model gradient explosion problem. Based on the electricity consumption data of 2007 (Australian Energy Market Operator) and a total of 336 power load data, a good load forecasting result is obtained, which can provide indirect support for power regulation and sustainable development of electricity. [ABSTRACT FROM AUTHOR]

Persistent PM 2.5 pollution poses a serious threat to human health. Developing an accurate urban regional PM 2.5 forecasting is of practical significance for environmental protection. However, previous studies have mostly focused on individual monitoring stations, neglecting the influence of neighboring stations, which limits forecasting accuracy. Additionally, the PM 2.5 of a single monitoring station cannot reflect the overall situation of a region. Therefore, this paper develops a novel PM 2.5 spatiotemporal forecasting framework that combines graph convolutional module, temporal convolutional module, linear module. It enables the forecasting of PM 2.5 concentrations at multiple stations and multiple time steps in the future. Concretely, we utilize a mixed graph convolutional network to extract the spatial features of PM 2.5. Then, an improved temporal convolutional network, the second-order residual temporal convolutional network, is developed to capture complex temporal features. Following the classical "linear and non-linear" modeling strategy, a linear module is added to the forecasting framework. Experiments on the real air pollution dataset from Beijing demonstrate that our framework outperforms the state-of-the-art baselines. • Extracting and fusing spatiotemporal features of PM2.5 is crucial for prediction. • A novel spatiotemporal forecasting framework is proposed. • A second-order residual TCN is developed to extract complex temporal features. • Experiments on real air pollution dataset show our framework's superiority. [ABSTRACT FROM AUTHOR]

Compositional data (CoDa) is a type of relative data reflecting the proportion of different units in an integrated system, which can reveal the internal structure of observed objects and has been widely used to analyze practical economic and management problems. However, the summation constraint limits the prediction of compositional data time series. Especially, with small samples and poor information characteristics of the data set, the difficulties are further exacerbated. Compared with real-valued time series analysis, fuzzy time series (FTS) mainly transforms initial observed data to be different status so that the historical evolution rules can be derived. As a result, forms of initial observed data can be concealed. Correspondingly, this paper aims to establish a multi-rule combination prediction of compositional data based on a multivariate fuzzy time series model, named GA-CoDa-MFTS. Firstly, with the interaction of components, the adjustment rules between components are explored by using the fuzzy logical relationship (FLR). Secondly, the Genetic algorithm (GA) is used to find the optimal weight of the predicted values under different rules and derive the combined predicted values. Finally, the prediction effectiveness of the model is verified by CRMSE and CMAPE through a numerical study. • The fuzzy time series model and the compositional data are integrated. • The adjustment rules are explored by using the fuzzy logical relationship. • Genetic algorithm is used to get the optimal weight of different rules. • The combination model composed of different rules has better prediction effect. [ABSTRACT FROM AUTHOR]

With the emergence of artificial intelligence, deep learning techniques have been widely deployed in forecasting stock markets. However, existing deep-learning-based models for news-based forecasts of stock trends are mostly black-box and difficult to explain. The procedure by which how final predictions are made within models keeps unknown, making it hard to interpret why one prediction should be better than the other. To provide explanations on predictions, this paper proposes to inject causal inference into model procedures and causally interpret predictions. We first generate a causal graph from financial news, and then integrate the information in the causal graph into a neural network model for stock trend prediction. Moreover, in order to better extract keywords from financial news we introduce a novel keyword extraction method named Distinguishable Word Filtering by Kolmogorov–Smirnov Test (DWF-KST). The experiment results on five financial datasets demonstrate that not only our proposed model explicitly provides an interpretation of prediction results, but also outperforms the state-of-art methods. the achieved results boost predictions of S&P 500 2-category from 89.7% to 97.4%, 3-category from 77.4% to 82.5%, and 5-category from 61.5% to 71.6%. For the other two indexes, the performances of Dow index improve from 86.2% to 90.2% and Nasdaq index improve from 76.4% to 78.9%. • We propose a method for stock trend prediction. • We use causal inference based on unstructured data. • Improve the interpretability of DL-based stock prediction models. • We propose DWF-KST for keyword selection in financial news. [ABSTRACT FROM AUTHOR]

Student performance prediction is a critical task in supporting decision-making for Intelligent Tutoring Systems (ITS). Correct predictions of student performance are prerequisites for ITS to supply intelligent services and optimize learning efficiency, e.g., recommending the most appropriate learning resources for each student. Existing methods mainly include cognitive diagnosis and knowledge tracing, both of which focus on students' cognitive modeling based on their interactions on a sequence of items and give predictions by assessing if their cognitive states can meet the item requirements. Specifically, cognitive diagnosis only considers students' global static cognitive states, while knowledge tracing focuses on students' local dynamics. However, both global and local features are critical for predicting student performance. Therefore, in this paper, we propose a novel G lobal and L ocal N eural C ognitive (GLNC) model to capture both global and local features in student-item interactions for more accurate predictions. Specifically, we first learn students' global cognitive level according to all student-item interactions. Then, we propose a self-attentive encoder based on the scaled dot-product attention mechanism to extract the local cognitive dynamics and the dependencies between students' recent interactions. Finally, to obtain better predictions, we design a fused gate based on the similarity between students' recently responded items and the item to be predicted to adaptively combine the global and local features. To evaluate the effectiveness of GLNC, we compare it with both cognitive diagnosis and knowledge tracing methods. All experiments are conducted on three public datasets that contain real student-item interactions on mathematics or language courses from various ITS. The experimental results demonstrate that GLNC achieves an average score of 0.7810 on the AUC metric, 0.7627 on the ACC metric, 0.3987 on the RMSE metric, 0.2023 on the r 2 metric, respectively achieving an average improvement of 1.84%, 1.07%, 1.87%, and 11.38% in contrast to existing state-of-the-art methods (i.e., NCD and LPKT). Moreover, we further analyze the performance of GLNC under different probabilities of guessing and slipping, the results indicate that GLNC is more robust against the influence of noisy data while considering both global and local features. Benefiting from the superior accuracy and stability, our proposed GLNC has a wide range of potential implications for ITS, which can be easily applied to improve students' learning efficiency and experience. • Cognitive diagnosis focuses on students' global static cognitive states. • Knowledge tracing measures students' local dynamics in learning. • It is critical to make trade-offs between global and local features. • Our method achieves more precise and robust results on performance prediction. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm designed to automatically merge predictions from a collection of individual prediction methods coded in R. The algorithm employs varying weights and decision rules to ascertain the optimal amalgamation of these methods, with the aim of forecasting historical time series data while minimizing human intervention. The algorithm serves as an automated component within the artificial intelligence toolkit. The proposed algorithm (al), denoted as "alPCA" is founded on principal component analysis (PCA), hence the acronym. Commencing with 52 configurations of 11 distinct methods available in R, we calculate several loss functions: specifically, scaled Mean Absolute Percentage Error (sMAPE) and Mean Absolute Scaled Error (MASE) for both fitting (Training Phase) and prediction (Validation Phase), along with Root Mean Squared Error (RMSE) and Overall Weighted Average (OWA) solely for prediction (Validation Phase). We then employ PCA to reduce the error matrix derived from this data to one or two dimensions. Subsequently, the methods are ranked based on their proximity to the highest score. A probability distribution is fitted to this proximity metric, and utilizing the percentiles of these values, the optimal methods for combination are selected. We propose three categories of weights derived from the PCA scores, encompassing the fitting sMAPE (Training Phase) and the prediction sMAPE (Validation Phase), to facilitate the amalgamation process. This approach is applied to seven distinct univariate time series across diverse domains, including automobile sales, electricity production, and CO2 levels. Additionally, a set of 100 random monthly series from the M4 competition is included in the analysis. To assess the predictive precision of our algorithm, we compare its performance against three widely utilized combined prediction algorithms available in R. We evaluate the outcomes in Test Phase (unseen data) using four distinct loss functions and conduct a sensitivity analysis to gauge the algorithm's robustness and efficacy across various specifications. [ABSTRACT FROM AUTHOR]

Machine learning techniques have been widely used and are mostly performed by predicting point estimation. Nevertheless, there are various scenarios that require more information beyond only point estimation. Probabilistic prediction is a typical research topic that provides probability distribution while predicting, which addressed with much attention recently. Most of the previous works make an assumption on the probabilistic distribution to a certain extent, which may lead to potential errors in the subsequent decision-making procedure. In this paper, we propose a distribution-free method for regression problems on real-value response under the probabilistic prediction framework and present an effective boosting-based method to perform the training process. Moreover, we introduce a further improved method to accomplish an unbiased mean estimation of the target distribution. Thorough experiments on multiple benchmark data are conducted to demonstrate the effectiveness of the proposed method with regard to different measures. • A distribution-free framework for probabilistic forecasting. • Use a mixture of Gaussian distribution as the target distribution. • An effective boosting-based strategy. • Providing unbiased mean estimation. [ABSTRACT FROM AUTHOR]

High-frequency financial data is more difficult to predict than low-frequency data because it possesses nonlinearity, nonstationarity, higher volatility, and long memory and is frequently accompanied by the jump phenomena. In this paper, the nonparametric regression (NR) model based on kernel function is used to fit the nonlinear relationship between the nonstationary series Y t and its lagging series to model the trend of high frequency financial time series. Furthermore, the deep learning LSTM (long short-term memory) model is applied to capture the high volatility and frequent jumps of high frequency financial data and to improve the forecasting accuracy of the residual series. The results demonstrate that the hybrid NR and LSTM model has greatly improved the forecasting accuracy in several evaluation criteria. In comparison to NR, support vector machine (SVM), LSTM, ARIMA and NR-SVM models, the mean absolute error (MAE) of NR-LSTM has reduced by 89.78%, 97.85%, 86.48%, 32.47% and 89%, respectively. In addition, we have constructed the trading strategy for the Shanghai-Shenzhen 300 index by using the NR-LSTM model. The NR-LSTM model can continue to provide good returns even during a bear market, which can serve as a guide for investors. Furthermore, the NR-LSTM model also exhibits the best forecasting effect when we model the high-frequency data of Ping An bank in China, the FTSE 100 index in the UK, and the S&P 500 index in the US. [ABSTRACT FROM AUTHOR]