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Accepted by: Zied Babai Exponential smoothing in state space form (ETS) is a popular forecasting technique, widely used in research and practice. While the additive error ETS models have been well studied, the multiplicative error ones have received much less attention in forecasting literature. Still, these models can be useful in cases, when one deals with positive data, because they are supposed to work in such situations. Unfortunately, the classical assumption of normality for the error term might break this property and lead to non-positive forecasts on positive data. In order to address this issue, we propose using Log-Normal, Gamma and Inverse Gaussian distributions, which are defined for positive values only. We demonstrate what happens with ETS(M,*,*) models in this case, discuss conditional moments of ETS with these distribution and show that they are more natural for the models than the Normal one. We conduct the simulation experiments in order to study the bias introduced by point forecasts in these models and then compare the models with different distributions. We finish the paper with an example of application, showing how pure multiplicative ETS with a positive distribution works. [ABSTRACT FROM AUTHOR]

This paper aims to determine whether there is a case for promoting a new benchmark for forecasting practice via the innovative application of generative artificial intelligence (Gen-AI) for predicting the future. Today, forecasts can be generated via Gen-AI models without the need for an in-depth understanding of forecasting theory, practice, or coding. Therefore, using three datasets, we present a comparative analysis of forecasts from Gen-AI models against forecasts from seven univariate and automated models from the forecast package in R, covering both parametric and non-parametric forecasting techniques. In some cases, we find statistically significant evidence to conclude that forecasts from Gen-AI models can outperform forecasts from popular benchmarks like seasonal ARIMA, seasonal naïve, exponential smoothing, and Theta forecasts (to name a few). Our findings also indicate that the accuracy of forecasts from Gen-AI models can vary not only based on the underlying data structure but also on the quality of prompt engineering (thus highlighting the continued importance of forecasting education), with the forecast accuracy appearing to improve at longer horizons. Therefore, we find some evidence towards promoting forecasts from Gen-AI models as benchmarks in future forecasting practice. However, at present, users are cautioned against reliability issues and Gen-AI being a black box in some cases. [ABSTRACT FROM AUTHOR]

The optimization of resources and reduction of costs through efficient inventory management are paramount to organizational success. This study undertakes a comparative analysis of two distinct forecasting methodologies, Exponential Smoothing (ES) and Gradient Boosting (GB), within the framework of materials forecasting aimed at inventory minimization. Our study introduces innovation by methodically scrutinizing these approaches within a unified framework, shedding light on their merits and shortcomings. This comparative analysis gives practitioners a practical roadmap for the optimal forecasting strategy to streamline inventory management operations. Methodologies are evaluated based on their efficiency in predicting material demand, encompassing metrics such as accuracy, computational efficiency, and suitability across various inventory management scenarios. Response surface methodology entails refining processes to modify factorial variables' configurations to attain a desired peak or trough in response. The SPSS results show that the ES method has 43.20%, surpassing the accuracy of the inventory optimization model, which stood at 65.08%. The response surface methodology results show that 45.20% profit was achieved for the variable and operational cost process parameters. This research seeks to unveil the traces of each method, facilitating decision-makers in selecting an optimal forecasting strategy tailored to their specific inventory management requirements. The analysis shows that the ES method surpasses the accuracy of the GB machine learning for material forecasting to minimize inventory. [ABSTRACT FROM AUTHOR]

Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics. Author summary: The US and European Covid-19 Forecast Hubs focus on metrics such as deaths, new cases, and hospital admissions, but do not offer measurements of hospital strain like critical care bed occupancy, which was essential for the provisioning of healthcare resources during the COVID-19 pandemic. Furthermore, forecasting support was only guaranteed on the national level leaving many countries to look elsewhere for valuable sub-national forecasts. In France statistical modelling approaches were proposed to anticipate hospital stain at the sub-national level but these were limited by a two-week forecast horizon. We present a sub-national French modelling framework and online application for anticipating hospital strain at the four-week horizon that can account for abrupt changes in key epidemiological parameters. It was the only publicly available real-time non-Markovian mechanistic model for the French epidemic when implemented in January 2021 and, to our knowledge, it still was at the time it stopped in early 2022. Further adaptations of this surveillance system can serve as an anticipation tool for hospital strain across sub-national localities to aid in the prevention of short-noticed ward closures and patient transfers. [ABSTRACT FROM AUTHOR]

One of the main objectives of the time series analysis is forecasting, so both Machine Learning methods and statistical methods have been proposed in the literature. In this study, we compare the forecasting performance of some of these approaches. In addition to traditional forecasting methods, which are the Naive and Seasonal Naive Methods, S/ARIMA, Exponential Smoothing, TBATS, Bayesian Exponential Smoothing Models with Trend Modifications and STL Decomposition, the forecasts are also obtained using seven different machine learning methods, which are Random Forest, Support Vector Regression, XGBoosting, BNN, RNN, LSTM, and FFNN, and the hybridization of both statistical time series and machine learning methods. The data set is selected proportionally from various time domains in M4 Competition data set. Thereby, we aim to create a forecasting guide by considering different preprocessing approaches, methods, and data sets having various time domains. After the experiment, the performance and impact of all methods are discussed. Therefore, most of the best models are mainly selected from machine learning methods for forecasting. Moreover, the forecasting performance of the model is affected by both the time frequency and forecast horizon. Lastly, the study suggests that the hybrid approach is not always the best model for forecasting. Hence, this study provides guidelines to understand which method will perform better at different time series frequencies. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation prediction is a significant research topic in photovoltaics due to the clean and pollution-free characteristics of solar energy, which have contributed to its popularity worldwide. Photovoltaic data, as a type of time series data, exhibit strong periodicity and volatility. Researchers typically employ time–frequency signal processing methods, like empirical mode decomposition (EMD), to smooth the data during the feature engineering stage. However, improper operations at this stage could result in information leakage. Unfortunately, many existing studies on photovoltaic prediction fail to provide sufficient details on how signal processing methods are used during model training. To address this issue, this paper proposes the similarity-day extension EMD that avoids information leakage. The proposed method is validated through experiments conducted on the PV dataset of the Desert Knowledge Australia Solar Center, using mainstream models such as GRU, LSTM, CNN-LSTM, LSTN-CNN, and Bi-LSTM. The experimental results demonstrate an average improvement of 3.67% in MAE and 5.71% in RMSE when using this method, thus verifying its feasibility and effectiveness. Moreover, the proposed method can be applied to other data processing methods that may suffer from information leakage. [ABSTRACT FROM AUTHOR]

In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant noise, complicating efforts to enhance forecasting precision. To address these challenges, this study introduces an innovative model. This model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to bifurcate the original load data into low- and high-frequency components. For the smoother low-frequency data, a temporal convolutional network (TCN) is utilized, whereas the high-frequency components, which encapsulate detailed load history information yet suffer from a lower fitting accuracy, are processed using an enhanced soft thresholding TCN (SF-TCN) optimized with the slime mould algorithm (SMA). Experimental tests of this methodology on load forecasts for the forthcoming 24 h across all seasons have demonstrated its superior forecasting accuracy compared to that of non-decomposed models, such as support vector regression (SVR), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), TCN, Informer, and decomposed models, including CEEMDAN-TCN and CEEMDAN-TCN-SMA. [ABSTRACT FROM AUTHOR]

Machine learning models perform satisfactorily in landslide displacement prediction, but they are generally black-box models that are difficult to gain the trust of decision-makers. Therefore, a three-stage prediction framework based on the Hodrick–Prescott (HP) filter, double exponential smoothing (DES), natural gradient boosting (NGBoost), and Shapley additive explanations (SHAP) was proposed. The framework quantifies the uncertainty in the predictions and provides fully transparent outputs. In the first stage, the HP filter decomposes cumulative displacements into trend and period displacements. The second stage uses DES and NGBoost to predict them separately. In the third stage, we compute the SHAP values of the features to analyze the impact of the features on the model output. It is applied to the Bazimen and Baishuihe landslides in the Three Gorges Reservoir area and compared with other literature. The results show that the framework can achieve high accuracy in both point and interval prediction, and its performance is similar to or even better than other models. And it is easier to operate and applicable to a wider range of people. Most importantly, the framework can interpret the model, allows users to verify the consistency of the model prediction basis with the landslide evolution mechanism, and reduces random errors by optimizing the data. Overall, the prediction framework can capture the complex nonlinear relationships between features, solving the current challenge of machine learning models that cannot be applied in the final optimization scenario due to the lack of convincing, and providing a reliable tool for intelligent disaster prevention. [ABSTRACT FROM AUTHOR]

Based on data from the Central Bureau of Statistics, the divorce rate in Indonesia shows a tendency to increase from year to year. Similar conditions were experienced by Cibinong district. The forecasting method that will be used in this study is the Single Moving Average and Winter's Exponential Smoothing. The results of forecasting the number of lawsuits in 2023 from July to December with movements with two obtained forecasts in July were 602, August 283, MAD value = 67.29, MSE value = 8.722, MAPE = 11.61, RMSE = 1.46, Accuracy value of 88.39%. Movements with four forecasting periods in July were 620, August 448, September 301, October 141. MAD value = 99.33, MSE value = 14,722, MAPE = 16.59, RMSE = 1.90, Accuracy value of 83.41%. Forecasting with the Winters Exponential Smoothing Method with Alpha: 0.1, Beta: 0.3 and Gamma: 0.5, the forecast results obtained in July were 504.10026, August 491.61306, September 663.18788, October 745.41004, November 732.42766 and December 732.10904. MAE value is 171.65116, MSE value is 686.63361, MAD value is 38.74, MSE value is 2.797 MAPE value is 6.0 and RMSE value is 0.83 and accuracy value is 94.00%. Based on the calculation results above, it is concluded that forecasting the number of divorce filings at Cibinong Religious Court from January 2018 to December 2023 with the Winter Exponential Smoothing method has MAD, MSE, MAPE, RMSE values smaller than values in the Single Moving Average method. Winter Exponential Smoothing method is more appropriate with an accuracy value of 94.00%. [ABSTRACT FROM AUTHOR]

Thailand's economy, like many other ASEAN countries, heavily relies on remittances from Thai overseas workers. Predicting the number of such workers in the coming years is crucial for stakeholders engaged in economic planning and growth. This study aimed to find a reliable statistical model for this purpose, utilizing data from pre-pandemic years to compare two predictive methods. The dataset spans 120 months, from January 2010 to December 2019, and was obtained from the Library System, Department of Employment of the Royal Thai Government. The chosen forecasting techniques are the Box-Jenkins method and Holt-Winter's Exponential Smoothing method. Primary parameters were first estimated based on the given data, and then evaluated through the Bayesian Information Criterion (BIC). To assess model performance, the dataset was split into two subsets. The primary parameters were applied to the first 60 months' data (January 2010 to December 2014) to estimate secondary parameters. The remaining 60 months' data were then used to evaluate how well each model predicts the response variable, measured by the prediction mean absolute error (PMAE) and prediction root mean squared error (PRMSE). Based on the findings, the Box-Jenkins method outperformed Holt-Winter's method in forecasting the monthly number of fresh Thai overseas workers. This study's insights can serve as a valuable template for predicting overseas workers' numbers in other ASEAN countries with socio-economic-cultural contexts similar to Thailand. Accurate predictions can aid decision-making and planning for sustainable economic development in the region. [ABSTRACT FROM AUTHOR]

As a heavy equipment distributor company with a widespread population of units across Indonesia, PT. Kobexindo Tractors Tbk holds a significant spare parts inventory to meet their customers' needs. However, efficiently and effectively managing spare parts inventory is a challenge the company faces. Over the period from 2016 to 2023, the company experienced an average annual loss of Rp. 1,176,438,113, due to the inadequate analysis of spare parts demand, which serves as a reference in the procurement process. To address this issue, this research focuses on developing a model that can generate accurate forecasts for spare parts inventory, particularly Jungheinrich parts, to support appropriate management decisions in the procurement process at the company. The Exponential Smoothing method is chosen for its ability to handle data with fluctuating patterns and trends. Based on a review of previous research studies, the Exponential Smoothing method has proven to be the most effective in forecasting spare parts inventory with a high level of accuracy. This study will compare the Simple Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing methods. The attributes included in this research are spare parts inventory data collected from the company during the period of 2016-2023, which will be processed using Exponential Smoothing techniques. The results of these calculations will be evaluated using the Root Mean Square Error (RSME) and Mean Absolute Percentage Error (MAPE) techniques to test the accuracy and performance of the forecasting models. The data ratio used in this research is 70% for training data and 30% for testing data. The prototype development is conducted using the Python programming language. The research results indicate that the Holts Winter Exponential Smoothing Model with Multiplicative Seasonality and Multiplicative Trend (Triple Exponential) is the best method among others, as seen from the modeling evaluation results as follows: 1) Train RSME (7.082307), a low RSME value on training data indicates that this model has a small used for training. 2) Test MAPE (6.343268), a low MAPE value on test data indicates that this model provides fairly accurate predictions in percentage terms of the actual values on the test data. 3) Test RSME Values (23.160521), a sufficiently low RSME value on test data indicates that this model also successfully generalizes well on unseen data.prediction error rate on the data [ABSTRACT FROM AUTHOR]

Indonesia is widely known as a country with rich biodiversity. Medicinal plants that thrive in Indonesia are utilized as traditional medicine locally known as "jamu". One of the islands famous for jamu production is Madura Island. As a well-known jamu producer, Madura Island are facing problems related to jamu production. Procurement of medicinal plants is not well controlled. There are no reports of spices procurement and production. When there is an increase in demand or sale of certain jamu, the stock of jamu is commonly inadequate/insufficient This may result in order cancellation. The solution to this problem is to create a production forecasting information system by using single exponential smoothing. The data used is a weekly report on the number of sales of 3 types of jamu from August to October 2024. Mean Absolute Percentage Error (MAPE) testing using an alpha value of 0.1 to 0.9 resulted in "high" accuracy and the forecasted values were close to the actual data values. [ABSTRACT FROM AUTHOR]

Renewable energy forecasting services comprise various modules for intra-day and day-ahead forecasts. This work specifically addresses day-ahead forecasts, utilizing specifications based on endogenous, historical measurements. These specifications are designed to be computationally efficient, requiring fewer input variables and less training data. Such weather-independent specifications serve as benchmarks against the more computationally demanding forecasts based on numerical weather predictions. A series of experiments, designed to simulate the real-world application of an online system, were conducted on sliding windows of back-contact photovoltaic (installed at KAUST, Saudi Arabia) output series, solar irradiance recorded in Hawaii, and simulated data. Our analysis evaluated 24 specifications, which are variants of (i) functional time series models (including two novel shrinkage procedures); (ii) time series nearest neighbor schemes; (iii) exponential smoothing procedures; (iv) autoregressive integrated moving average processes; (v) automatic techniques based on time series decomposition; and (vi) the persistence model. In addition to employing outlier-robust accuracy metrics, such as mean absolute error, our evaluation also prioritized prediction-interval accuracy, quantified by the mean scaled interval score. Our findings suggest that practitioners can achieve significant improvements over the persistence model by forecasting daily profiles using adaptive nonparametric or functional data analysis-based procedures. Moreover, applying shrinkage to nearest neighbor (NN) forecasts toward smooth, average daily profiles significantly enhances NN performance. Conversely, some popular, computationally intensive models fail to perform adequately to justify their additional cost. [ABSTRACT FROM AUTHOR]

Exponential smoothing is one of the most widely used forecasting methods for univariate time series data. Based on the difference between protected and confidential time series data, we derive theoretical bounds for the absolute change to forecasts generated from additive exponential smoothing models. Given time series data up to time t, we discover a functional form of robust bounds for the absolute change to forecasts for any T ≥ t + 1 , which can be represented as a compact form of geometric series. We also find robust bounds for the Change in Mean Absolute Error ( Δ MAE ) and Measured Mean Absolute Error (MMAE). [ABSTRACT FROM AUTHOR]

The number of deaths due to road accident is increasing day by day and has become an alarming global problem over the decades. India, with her rising motorization is no stranger to this global catastrophe. In this paper two relatively simple yet powerful and versatile techniques for forecasting time series data, autoregressive integrated moving average method (ARIMA) and exponential smoothing method are used to forecast the number of deaths due to road accidents in India from the year 2022-- 2031. The results based on the two methods are compared and it is found that they are in sync with each other and pre-existing literature. Furthermore, this is a unique attempt to use two time series analysis techniques on the same data and carry out a comparative analysis. The data was collected from the annual report of Ministry of Road Transport and Highways, India (2020) and Accidental Deaths & Suicides in India (ADSI) Report of National Crime Record Bureau (2021). After examining all the probable models, it is observed that ARIMA (2, 2, 2) model and exponential smoothing (M, A, N) model are suitable for the given data. Amongst the two, ARIMA (2, 2, 2) model has a lower AIC and BIC value. Thus, this comes out to be the best model as per our model selection criterion. Further, the study also reveals an upward trend of number of road accidental deaths for the upcoming 10 years in India. [ABSTRACT FROM AUTHOR]

This article presents an approach to forecasting count time series with a form of exponential smoothing built from observation‐driven models. The proposed method is easy to implement and simple to interpret. A variant of the approach is also proposed to handle the impact of outliers on the forecast. The performance of the methodology is studied with simulations and illustrated with an analysis of the number of monthly cases of dengue fever observed in Italy for the years 2008–2021. An R package is made available to enable the reader to reproduce the results discussed in the article. [ABSTRACT FROM AUTHOR]

Objective: To establish the exponential smoothing prediction model and SARIMA model to predict the number of inpatients in a third-class hospital in Zhejiang Province, and evaluate the prediction effect of the two models, and select the best number prediction model. Methods: The data of hospital admissions from January 2019 to September 2022 were selected to establish the exponential smoothing prediction model and the SARIMA model respectively. Then compare the fitting parameters of different models: R2_adjusted, R2, Root Mean Square Error (RMSE)、Mean Absolute Percentage Error (MAPE)、Mean Absolute Error(MAE) and standardized BIC to select the best model. Finally, the established model was used to predict the number of hospital admissions from October to December 2022, and the prediction effect of the average relative error judgment model was compared. Results: The best fitting exponential smoothing prediction model was Winters Addition model, whose R2_adjusted was 0.533, R2 was 0.817, MAPE was 6.133, MAE was 447.341. The best SARIMA model is SARIMA(2,2,2)(0,1,1)12 model, whose R2_adjusted is 0.449, R2 is 0.199, MAPE is 8.240, MAE is 718.965. The Winters addition model and SARIMA(2,2,2)(0,1,1)12 model were used to predict the number of hospital admissions in October-December 2022, respectively. The results showed that the average relative error was 0.038 and 0.015, respectively. The SARIMA(2,2,2)(0,1,1)12 model had a good prediction effect. Conclusion: Both models can better fit the number of admissions, and SARIMA model has better prediction effect. [ABSTRACT FROM AUTHOR]

Electricity generation in Taiwan is mainly from thermal, nuclear, and renewable sources. With nearly four decades of monthly data (from January 1982 to January 2022) used in analytics, the forecasting results reveal the patterns of electricity generation in Taiwan. Exponential smoothing and Holt–Winters seasonal models are used for time‐series forecasting, and scenario analysis is applied to simulate possible paths for nuclear phase‐out in Taiwan by 2025. Taking the fact of Taiwan's lack of energy independence into account, transition for nuclear phase‐out may require a substantial leap to meet the demand. The feasibility of electricity generation using current thermal power capacity and renewable energy remain challenging. [ABSTRACT FROM AUTHOR]

The importance of forecasting in the energy sector as part of electrical power equipment maintenance encourages researchers to obtain accurate electrical forecasting models. This study investigates simple to complex automatic methods and proposes two weighted ensemble approaches. The automated methods are the autoregressive integrated moving average; the exponential smoothing error–trend–seasonal method; the double seasonal Holt–Winter method; the trigonometric Box–Cox transformation, autoregressive, error, trend, and seasonal model; Prophet and neural networks. All accommodate trend and seasonal patterns commonly found in monthly, daily, hourly, or half-hourly electricity data. In comparison, the proposed ensemble approaches combine linearly (EnL) or nonlinearly (EnNL) the forecasting values obtained from all the single automatic methods by considering each model component's weight. In this work, four electrical time series with different characteristics are examined, to demonstrate the effectiveness and applicability of the proposed ensemble approach—the model performances are compared based on root mean square error (RMSE) and absolute percentage errors (MAPEs). The experimental results show that compared to the existing average weighted ensemble approach, the proposed nonlinear weighted ensemble approach successfully reduces the RMSE and MAPE of the testing data by between 28% and 82%. [ABSTRACT FROM AUTHOR]

The transport truck is one of the important equipment for open-pit mines, and predicting the truck's fault time is of great significance in improving the economic benefits of open-pit mines. In this paper, we discuss the reason for the large prediction error of the exponential smoothing method. Then, we propose a novel nonlinear exponential smoothing method (ESNN) for predicting the truck's fault time, and demonstrate the equivalence between our approach and the neural network structure. Finally, based on the augmented Lagrange function, the solving method of ESNN is proposed. We conduct experiments on real-world datasets and our results demonstrate the effectiveness of ESNN in comparison to existing state-of-the-art methods. Our approach makes it easier for maintenance personnel to predict fault situations in advance and provides a basis for enterprises to develop preventive maintenance plans. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the railway passenger volume data are abnormal due to holidays and major events interfering with the prediction accuracy, the spline interpolation method is introduced to replace the abnormal passenger volume data. In addition, an improved particle swarm optimization (IPSO) is proposed to optimize the gradient difference acceleration law to combine and improve the predicted value and further improve the prediction accuracy of the railway passenger traffic. Finally, taking Beijing as the research object, the Holt exponential smoothing method and the BP neural network are selected to verify the effect of spline interpolation and IPSO-gradient difference acceleration law on prediction accuracy. The research results show that the spline interpolation method has a better prediction effect after processing abnormal passenger traffic data, and the improved particle swarm algorithm also shows better optimization ability and convergence speed when solving the double difference postulate. In comparison with the BP neural network, Holt exponential smoothing, simple averaging, and conventional redifference approaches, the IPSO-redifference acceleration method achieves a superior prediction performance, and the absolute values of the forecast error are reduced by 3.320%, 1.518%, 2.419%, and 0.602%. [ABSTRACT FROM AUTHOR]

Context and objectives: Climate extremes associated with wet and dry conditions are one of the main causal elements of the disasters that adversely impact Uganda's agriculturally based economy. However, the lack of reliable forecast records for wet and dry conditions remains a challenge at a local scale. Given the proliferation of geostatistical forecast algorithms, this study assessed the suitability of three forecast algorithms: forest-based, exponential smoothing and curve fitting in forecasting wet and dry conditions at a local scale in Uganda. Methodology: The CHIRPS satellite gridded datasets for the variable short rains season of September, October, and November (SON) for the 1981-2020 historical period were used to develop the forecasts and the 2021-2022 period was used to validate the forecasts. The Standardized Precipitation Index (SPI) derived from the CHIRPS dataset was used as the proxy for wet and dry conditions. The three algorithms were assessed by subjecting them to the SPI space-time cube structure in the ArcGIS environment. Using the resultant Forecast and Validation Root Mean Square Error (FRMSE and VRSME respectively), forecast models were then generated for each of the algorithms. A comparison of the least VRSME at every locality from any of the three algorithms was then used to build a joint forecast model. Results: Results from the three algorithms demonstrate that each locality experiences an independent forecast regardless of the influence of the immediate neighbourhood, and by 2025, the majority of the localities will experience moderately wet conditions during the SON season. The forest-based forecast, exponential smoothing, curve fitting and the combined least VRMSE forecast produced a best value of R2=0.33, 0.48, 0.4 and 0.62 respectively upon validation of the exact value. However, for values within the 95% confidence interval band, an R2 =0.89, 0.83 was realized for the forest-based and exponential smoothing. Based on the behavioural performance of the algorithms, results further reveal that most of the localities in the study area exhibit complex patterns and can best be predicted by forest-based algorithms. The results from this study support the motive of the Sustainable Development Goal (SDG) 13 by enabling the communication of empirical studies on locally determined climate contributions. The knowledge gained regarding localized wet and dry conditions prediction will help to increase the capacity of local governance and decision-making organizations that adopt and put into practice local disaster risk reduction initiatives. Further research is needed to assess the driving factors behind the pattern behaviours at the different localities. [ABSTRACT FROM AUTHOR]

Metals are indispensable raw materials for industry and have strategic importance in economic development. The price forecasting of metals is crucial for the production sector and production policies of countries. The paper presents the application of various exponential smoothing methods to metal spot price forecasting. Aluminum, copper, lead, iron, nickel, tin, and zinc prices were analyzed by using yearly data from 1990 to 2021. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) values of the models were obtained and their performances were compared to determine the appropriate model for each metal price. These metal prices were forecasted up to 2030 by using the best-fitted models. [ABSTRACT FROM AUTHOR]

With the increase in population and the progress of industrialization, the rational use of energy in heating systems has become a research topic for many scholars. The accurate prediction of heat load in heating systems provides us with a scientific solution. Due to the complexity and difficulty of heat load forecasting in heating systems, this paper proposes a short-term heat load forecasting method based on a Bayesian algorithm-optimized long- and short-term memory network (BO-LSTM). The moving average data smoothing method is used to eliminate noise from the data. Pearson's correlation analysis is used to determine the inputs to the model. Finally, the outdoor temperature and heat load of the previous period are selected as inputs to the model. The root mean square error (RMSE) is used as the main evaluation index, and the mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2) are used as auxiliary evaluation indexes. It was found that the RMSE of the asynchronous length model decreased, proving the general practicability of the method. In conclusion, the proposed prediction method is simple and universal. [ABSTRACT FROM AUTHOR]

Ross River virus (RRV) disease is one of the most epidemiological mosquito-borne diseases in Australia. Its major consequences on public health require building a precise and accurate model for predicting any forthcoming outbreaks. Several models have been developed by machine learning (ML) researchers, and many studies have been published as a result. Later, deep learning models have been introduced and shown tremendous success in forecasting, mainly the long short-term memory (LSTM), which performs significantly better than the traditional machine learning approaches. There are four common problems that previously developed models need to solve. They are exploding gradient, vanishing gradient, uncertainty and parameter bias. LSTM has already solved the first two problems, i.e. exploding and vanishing gradient problems, and the remaining two are overcome by n -LSTM. However, developing a prediction model for the RRV disease is a challenging task because it presents a wide range of symptoms, and there needs to be more accurate information available on the disease. To address these challenges, we propose a data-driven ensemble deep learning model using multi-networks of LSTM neural network for RRV disease forecasting in Australia. Data is collected between 1993 and 2020 from the Health Department of the Government of Australia. Data from 1993 to 2016 is taken to train the model, while the data of 2016–2020 is used as a test dataset. Previous research has demonstrated the efficacy of both ARIMA and exponential smoothing techniques in the field of time-series forecasting. As a result, our study sought to evaluate the performance of our proposed model in comparison to these established parametric methods, including ARIMA and ARMA, as well as the more recent deep learning approaches such as encoder–decoder and attention mechanism models. The results show that n -LSTM achieves higher accuracy and has a less mean-square error. We have also discussed the comparison of the models in detail. Such forecasting gives an insight into being well prepared and handling the situation of the outbreak. [ABSTRACT FROM AUTHOR]

This study aims to investigate whether combining forecasts generated from different models can improve forecast accuracy rather than individual models using the hierarchical time series. Various approaches of hierarchical forecasting have been considered; a bottom-up, top-down, and an optimal combination approach. Autoregressive moving averages (ARIMA) and exponential smoothing (ETS) were used as forecasting models in creating forecasting for all levels in the hierarchy to show the effect of different forecasting methods for each hierarchical model. The results indicated that the Minimum Trace Sample estimator (MinT-Sample) and the bottom-up approaches with the ARIMA model have good predictive performance than other approaches. Moreover, the forecasts from the MinT-Sample and bottom-up approaches were combined using five different combining methods. The experimental results showed that the (AC) method is superior to all other combining methods and more accurate than other individual models at level zero (international total trade in Egypt) and level one (total exports, and total imports). So, combining forecasts generated from different models by hierarchical time series leads to more accurate forecasting of the value of imports and exports which will improve the overall international trade performance, and that is through using the forecasting values of imports and exports to plan for improving the trade balance and drawing up a more efficient production policy. Finally, the study recommends using hierarchical forecasting methods in the areas of international trade, and the Ministry of Commerce and Industry could adopt the results of this study to produce precise forecasts for international trade. Moreover, the results of this study are to be a guide for the researchers to apply these approaches in other fields to improve the performance of forecasting. [ABSTRACT FROM AUTHOR]

Background: Accelerated globalization especially in the late 1980s has provided opportunities for economic progress in the world of emerging economies. The BRICS nations' economies are distinguishable from other emerging economies due to their rate of expansion and sheer size. As a result of their economic prosperity, health spending in the BRICS countries has been increasing. However, health security is still a distant dream in these countries due to low public health spending, lack of pre-paid health coverage, and heavy out-of-pocket spending. There is a need for changing the health expenditure composition to address the challenge of regressive health spending and ensure equitable access to comprehensive healthcare services. Objective: Present study examined the health expenditure trend among the BRICS from 2000 to 2019 and made predictions with an emphasis on public, pre-paid, and out-of-pocket expenditures for 2035. Methods: Health expenditure data for 2000–2019 were taken from the OECD iLibrary database. The exponential smoothing model in R software (ets ()) was used for forecasting. Results: Except for India and Brazil, all of the BRICS countries show a long-term increase in per capita PPP health expenditure. Only India's health expenditure is expected to decrease as a share of GDP after the completion of the SDG years. China accounts for the steepest rise in per capita expenditure until 2035, while Russia is expected to achieve the highest absolute values. Conclusion: The BRICS countries have the potential to be important leaders in a variety of social policies such as health. Each BRICS country has set a national pledge to the right to health and is working on health system reforms to achieve universal health coverage (UHC). The estimations of future health expenditures by these emerging market powers should help policymakers decide how to allocate resources to achieve this goal. [ABSTRACT FROM AUTHOR]

Link prediction accuracy in temporal networks is easily affected by the time granularity of network snapshots. This is due to the insufficient information conveyed by snapshots and the lack of temporal continuity between snapshots. We propose a temporal network link prediction method based on the optimized exponential smoothing model and node interaction entropy (OESMNIE). This method utilizes fine-grained interaction information between nodes within snapshot periods and incorporates the information entropy theory to improve the construction of node similarity in the gravity model as well as the prediction process of node similarity. Experiment results on several real-world datasets demonstrate the superiority and reliability of this proposed method in adapting to link prediction requirements over other methods across different time granularities of snapshots, which is essential for studying the evolution of temporal networks. [ABSTRACT FROM AUTHOR]

The models for forecasting time series with seasonal variability can be used to build automatic real-time control systems. For example, predicting the water flowing in a wastewater treatment plant can be used to calculate the optimal electricity consumption. The article describes a performance analysis of various machine learning methods (SARIMA, Holt-Winters Exponential Smoothing, ETS, Facebook Prophet, XGBoost, and Long Short-Term Memory) and data-preprocessing algorithms implemented in Python. The general methodology of model building and the requirements of the input data sets are described. All models use actual data from sensors of the monitoring system. The novelty of this work is in an approach that allows using limited history data sets to obtain predictions with reasonable accuracy. The implemented algorithms made it possible to achieve an R-Squared accuracy of more than 0.95. The forecasting calculation time is minimized, which can be used to run the algorithm in real-time control and embedded systems. [ABSTRACT FROM AUTHOR]

One of the Indonesian government's responses to the COVID-19 pandemic is making policies related to restrictions on public services which affects the organizational resilience of the Rehabilitation Center of the National Narcotics Board (BNN). This research aimed to determine the historical pattern of the influence of public service policies during the COVID-19 pandemic on the client population, to forecast the client population for 3 (three) months ahead, and to analyze strategies for anticipating rehabilitation services at the Rehabilitation Center of BNN. This research method is quantitative by using a moving average (MA) and exponential smoothing forecasting model. Based on the validity test, MA is the best forecasting model, which indicates a possibility of a spike in male clients with the same amount in the pre-pandemic period, as many as 310 people, and the average female client is 7 people. Meanwhile, adolescent clients show inaccurate prediction results with MAPE 149.825. Strategies that can be implemented to anticipate a spike in the number of clients if it reaches the highest forecasting point are: increasing the budget, modifying the rehabilitation program for female and adolescent clients, a balanced staff composition, and the availability of facilities and infrastructure. [ABSTRACT FROM AUTHOR]

This paper introduces an adaptive interval fuzzy modeling method using participatory learning and interval-valued stream data. The model is a collection of fuzzy functional rules in which the rule base structure and the parameters of the rules evolve simultaneously as data are input. The evolving nature of the method allows continuous model adaptation using the stream interval input data. The method employs participatory learning to cluster the interval input data recursively, constructs a fuzzy rule for each cluster, uses the weighted recursive least squares to update the parameters of the rule consequent intervals, and returns an interval-valued output. The method is evaluated using actual data to model and forecast the daily lowest and highest prices of the four most traded cryptocurrencies, BitCoin, Ethereum, XRP, and LiteCoin. The performance of the adaptive interval fuzzy modeling is compared with the adaptive neuro-fuzzy inference system, long short-term memory neural network, autoregressive integrated moving average, exponential smoothing state model, and the naïve random walk methods. Results show that the suggested interval fuzzy model outperforms all these methods in predicting prices in the digital coin market, especially when considering directional accuracy measure. [ABSTRACT FROM AUTHOR]

A prediction interval (PI) method is developed to quantify the model uncertainty of embankment settlement prediction. Traditional PIs are constructed based on specific past period information and remain unchanged; hence, they neglect discrepancies between previous calculations and new monitoring data. In this paper, a real-time prediction interval correction method is proposed. Time-varying PIs are built by continuously incorporating new measurements into model uncertainty calculations. The method consists of trend identification, PI construction, and real-time correction. Primarily, trend identification is carried out by wavelet analysis to eliminate early unstable noise and determine the settlement trend. Then, the Delta method is applied to construct PIs based on the characterized trend, and a comprehensive evaluation index is introduced. The model output and the upper and lower bounds of the PIs are updated by the unscented Kalman filter (UKF). The effect of the UKF is compared with that of the Kalman filter (KF) and extended Kalman filter (EKF). The method was demonstrated in the Qingyuan power station dam. The results show that the time-varying PIs based on trend data are smoother than those based on original data with better evaluation index scores. Also, the PIs are not affected by local anomalies. The proposed PIs are consistent with the actual measurements, and the UKF performs better than the KF and EKF. The approach has the potential to provide more reliable embankment safety assessments. [ABSTRACT FROM AUTHOR]

This study applies statistical methods and deep learning techniques to forecast the daily average PM2.5 concentration in northern Thailand, where the concentration is usually high and exceeds the safe level. The data used in the analysis are collected from January 2018 to December 2020 from 16 air monitoring stations. The statistical methods used are Holt-Winters exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), and dynamic linear model (DLM). The deep learning techniques considered in this study are the recurrent neural network (RNN) and long-short term memory (LSTM). To compare the predictive performance of both methods, we use the root mean square error (RMSE). The result indicates that statistical methods, especially ARIMA, perform better than the deep learning techniques in most stations. Moreover, LSTM tends to provide higher accuracy than the RNN, especially with more number of nodes. [ABSTRACT FROM AUTHOR]

Streamflow forecasting process exhibited highly nonstationary and stochastic pattern, thus not easy to be done with simple models. There is a need to develop an efficient and precise streamflow forecasting system which is vital for water management at hydrological infrastructures like Aswan High Dam (AHD). As the decision makers will be able to decide on water allocation for different purposes such as irrigation, domestic and industrial uses. This study explores the potential of AI model: nonlinear autoregressive neural network (NAR) in performing inflow forecasting to AHD. The dataset of past 130 years of Nile River discharge rate was used for the network development as well as evaluation of models' performance. This study also proposes an integration process of NAR with Holt-Winters exponential smoothing to improve the accuracy of the model. To determine the models' performance, different indicators were employed and calculated (MAE, MAPE, RMSE, R2). The results were compared to identify the optimal network architecture. The results show that the NAR models are capable of predicting the future values of AHD inflow in monthly time steps accurately. For standard NAR model, the root mean squared error (RMSE) was 2.0072, and the coefficient of determination (R2) between recorded and forecasted values was 0.9152. Values of RMSE = 1.5421 and R2 = 0.9760 and RMSE = 1.0843 and R2 = 0.9823 were obtained by NAR-SES and NAR-HW models respectively. The results reveal that combination of Holt-Winters exponential smoothing with NAR significantly improved the precision beyond the standard model. This study proved that NAR neural networks can be useful to address streamflow forecasting problems. [ABSTRACT FROM AUTHOR]

To reasonably ensure good groundwater quality for the safety of users, it is necessary to monitor the evolution of physicochemical parameters of water. This study assessed the evolution of groundwater quality in Xianyang City (Central China) for 6 years and forecasted the water quality index (WQI) using the automatic exponential smoothing model (AESM). The results revealed that pH, Mn, NH4+, HCO3− and Ca2+ increased significantly within 6 years. However, there was a slight decrease in K+, NO3−, SiO2, and Mg2+. Over the course of 6 years, seven out of fourteen wells maintained excellent or good water quality, whereas groundwater quality in the remaining wells fluctuated from excellent to good or from good to poor. The WQI index increased for nine wells, and five of them including well 173, well 457, well 46, well 26, and well C4 manifested an exponential increase of WQI, which was mainly caused by high concentrations of Mn, NH4+, and NO3−. The forecast accuracy verified by MASE and sMAPE, which ranged from 0.35 to 1.06 and from 0.04 to 0.69, respectively, demonstrated that the predictions were reliable. The AESM was tested in groundwater quality prediction, indicating that it might be used for actual scientific studies. [ABSTRACT FROM AUTHOR]

The forecasting problems can be effectively solved by using artificial neural networks. Classical forecasting methods are not sufficient to forecast nonlinear and complex time series structures such as a stock exchange time series. In this study, a new hybrid recurrent artificial neural network is proposed for nonlinear time series forecasting. The proposed network is a combination of simple exponential smoothing and the single multiplicative neuron model. The combination weights are also weights in the proposed network, and they are automatically estimated in the training processes. The training of the proposed network is achieved by using a particle swarm optimization-based training algorithm. The training algorithm uses restarting and early stopping strategies to prevent overfitting problems. The proposed network is applied to S&P500, Dow Jones stock exchange data sets, minimum temperature data and wind speed data. The performance of the proposed method is superior to two popular deep artificial neural networks and a high-order artificial neural network. [ABSTRACT FROM AUTHOR]

Rotary kiln temperature forecasting plays a significant part of the automatic control of the sintering process. However, accurate forecasts are difficult owing to the complex nonlinear characteristics of rotary kiln temperature time series. With the development of chaos theory, the prediction accuracy is improved by analyzing the essential characteristics of time series. However, the existing prediction methods of chaotic time series cannot fully consider the local and global characteristics of time series at the same time. Therefore, in this study, the global recurrence plot (GRP)-based generative adversarial network (GAN) and the long short-term memory (LSTM) combination method, named GRP-lstmGAN, are proposed, which can effectively display important information about time scales. First, the data is subjected to a series of pre-processing operations, including data smoothing. Then, transforming one-dimensional time series into two-dimensional images by GRP makes full use of the global and local information of time series. Finally, the combination of LSTM and improves GAN models for temperature time series prediction. The experimental results show that our model is better than comparison models. [ABSTRACT FROM AUTHOR]

The COVID19 pandemic has significantly affected the performance of the transport sector and its overall intensity. Reduced mobility has a large impact on the number of road accidents. The aim of this study is to forecast the number of road accidents in Poland and to assess the impact of the COVID19 pandemic on the variation in road crashes. For this purpose, day-wise historical crash data from 2011 onwards have been collected and analysed. Based on real historical field data, the future has been forecasted for both pandemic and nonpandemic variants. Forecasting of the number of accidents has been carried out using selected time series models and exponential models. Based on obtained data, it can be stated that pandemic resulted in a decrease in number of road accidents in Poland with ranges of reduction varying from 11% to 30% based on different days of week. Most visible decrease is observed on 3 days viz. Monday, Wednesday, and Saturday. Further, the projections show that in view of the current situation one may expect further decrease in the number of road accidents in Poland. [ABSTRACT FROM AUTHOR]

Suicide prevention efforts require conscious coordination and close collaboration between health agencies. They should be based on an understanding of the true picture of the prevalence of this phenomenon in a particular area, the characteristics of the dynamics of changes in the frequency of suicides, and high-risk factors, namely age, gender, climatic and social components. The purpose of this study was an attempt to create for each region of Ukraine a statistical model of the dynamics of the frequency of completed suicides depending on the time component (months of the year) and to build on its basis a forecast of the dynamics of the indicator of the number of deaths due to intentional self-harm. For this, the autocorrelation of absolute indicators was carried out and correlograms of time series of indicators of the deaths' number due to intentional self-harm were constructed. The obtained correlograms had sufficiently pronounced features, which made it possible to structure them into 4 separate groups. For further analysis, we used the time series of the areas that made up the first two groups, characterized by a trend and seasonality. For further analysis, only models of exponential smoothing of the time series of areas were used, whose indicators of Ljung-Box Q-statistics, coefficient of determination, mean modulus of error, and smoothing of the mean were in an acceptable range. Based on the created time series model, it is possible to assume that the period from August 2021 to September 2022, will increase in the absolute indicator of the number of deaths due to intentional self-harm in the spring months and, for most regions, in January. For the autumn period, on the contrary, a decrease in the number of completed suicides is characteristic. the characteristics of the time series models for a whole group of regions did not allow us to use them to build a forecast. These regions are represented by two different geographical groups – a group of regions of Western Ukraine and three Black Sea regions. Created for each region of Ukraine, a statistical model of the frequency dynamics of the completed suicides depending on the time component (months of the year) made it possible to build an annual forecast for the number of deaths dynamics due to intentional self-harm. Longer-term forecasts are possible by analyzing more data. [ABSTRACT FROM AUTHOR]

Time series are essential for anticipating various claims payment applications. For insurance firms to prevent significant losses brought on by potential future claims, the future values of predicted claims are crucial. Additionally, the ideal parameter is chosen artificially. By using a genuine application, the proposed model's utility is demonstrated. Additionally, the ideal parameter is chosen artificially. By using a genuine application, the proposed model utility is demonstrated. Also, the single exponential smoothing model is used for prediction under the Holt-Winters' additive algorithm. [ABSTRACT FROM AUTHOR]

Fuzzy time series is widely used in forecasting time series data in linguistic forms. Implementing the intuitionistic fuzzy sets (IFS) in fuzzy time series can better handle uncertainties and vagueness in the time series data. However, the time series data always fluctuate randomly and cause drastic changes. In this study, the 4253HT smoother is integrated with the intuitionistic fuzzy time series forecasting model to improve the forecasting accuracy. The proposed model is implemented in predicting the Malaysian crude palm oil prices. The data are firstly smoothed, and followed with the fuzzification process. Next are the transformation of fuzzy sets into IFS and the de-ifuzzification via equal distribution of hesitancy. The forecasted data are calculated based on the defuzzified values considering the new membership degrees of the IFS after de-i-fuzzification. The results show that the integrated model produces a better forecasting performance compared to the common intuitionistic fuzzy time series forecasting model. In the future, the integration of the data smoothing should be considered before the forecasting of data using fuzzy time series could be performed. [ABSTRACT FROM AUTHOR]

At present, the existing prediction algorithm of a port cargo throughput neglects the correction of the initial value of the cargo series data model, which leads to a large error in a port cargo throughput prediction. Therefore, a prediction algorithm of a port cargo throughput based on the ES-Markov model is designed. A decompose function is used to decompose the time series of a port cargo throughput, and the trend elements of a port cargo throughput are divided into long-term trend, seasonal trend, fluctuation trend, and irregular trend. In this study, the ES-Markov model is introduced, and the initial prediction is obtained by using the cubic exponential smoothing method, and the state transition matrix is obtained by the Markov principle. Based on the results of the time-series analysis and the ES-Markov model, the prediction algorithm of a port cargo throughput is designed. In the experimental design, the Elman neural network is used to construct an experimental sample data model. The monthly cargo throughput data of a certain port for eight months from May 2020 to December 2020 are collected and sorted according to the time series. The experimental results show that the prediction results of the proposed algorithm are closer to the actual value and the fluctuation of the prediction results is less than that of the reference. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has strong effects on most health care systems. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and 75% in 2020 with the effects of the COVID-19 pandemic. The best models substantially outperformed a one-step seasonal naïve forecast (seasonal mean absolute scaled error (sMASE) 2019: 0.59, 2020: 0.76). The best model in 2019 was a machine learning model (elastic net, mean absolute error (MAE): 7.25). The best model in 2020 was a time series model (exponential smoothing state space model with Box-Cox transformation, ARMA errors and trend and seasonal components, MAE: 10.44). Models forecasting admissions one week in advance did not perform better than monthly and yearly models in 2019 but they did in 2020. The most important features for the machine learning models were calendrical variables. Model performance did not vary much between different modelling approaches before the COVID-19 pandemic and established forecasts were substantially better than one-step seasonal naïve forecasts. However, weekly time series models adjusted quicker to the COVID-19 related shock effects. In practice, multiple individual forecast horizons could be used simultaneously, such as a yearly model to achieve early forecasts for a long planning period and weekly models to adjust quicker to sudden changes. [ABSTRACT FROM AUTHOR]

The analysis of the basic characteristics of various research methods is highly needed to predict the residual life of the pipeline accurately, help managers understand the operational risks, and provide a reference for developing pipeline transportation and maintenance inspection plans and anti-corrosion measures. Based on a comprehensive investigation of the existing research on the residual life of the pipeline, this paper finds that the current mainstream life prediction method, based on historical statistical data, has the shortcomings of inconsistent modeling methods, inconsistent basic data, and a lack of comparative evaluation among methods. Moreover, considering the in-depth study of BP neural network modeling, grey theory modeling, time series modeling, and exponential smoothing modeling, optimal prediction models using different methods based on the same historical data are established. These optimal modeling methods are discussed, and the feasible modeling path for the accurate prediction of the pipeline's residual life is given by comparing the prediction accuracy of each model. In addition, the findings serve as a guide for developing an anti-corrosion strategy by highlighting the contribution of the prediction results of the residual life to pipeline decision-making. By comparison, it is found that the accuracy of the four prediction models is as follows: the grey theory prediction model, the exponential smoothing prediction model, the BP neural network prediction model, and the time series prediction model, from high to low, respectively. [ABSTRACT FROM AUTHOR]