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Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture. [ABSTRACT FROM AUTHOR]

China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity. The country has entered a new development stage designed to achieve its second centenary goal. However, ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty. In this paper, the Liupan Mountain Region of China was used as a case study, and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area. The indicator data for returning to ecological poverty from 2011–2020 were collected and summarized in three dimensions: ecological, economic and social. The autoregressive integrated moving average model (ARIMA) time series and exponential smoothing method (ES) were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties (districts) in the Liupan Mountain Region for 2021–2030. The back propagation neural network (BPNN) and geographic information system (GIS) were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty (RREP index). The results show that 1) ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region. 2) The RREP index for the 61 counties (districts) exhibited a downward trend from 2021–2030. The RREP index declined more in medium- and high-risk areas than in low-risk areas. From 2021 to 2025, the RREP index exhibited a slight downward trend. From 2026 to 2030, the RREP index was expected to decline faster, especially from 2029–2030. 3) Based on the RREP index, it can be roughly divided into three types, namely, the high-risk areas, the medium-risk areas, and the low-risk areas. The natural resource conditions in low-risk areas of returning to ecological poverty, were better than those in medium- and high-risk areas. [ABSTRACT FROM AUTHOR]

In this paper, an error-correcting ensemble model combining empirical mode decomposition and reconstruction combined with statistical methods is proposed to improve the prediction accuracy of a high concentration of particulate matter with diameters of 2.5 μm or less (PM2.5) around a tailings reservoir. The proposed prediction model consists of a prediction model of a high concentration of PM2.5 and an error correction model. The optimal prediction model is obtained by comparing the two indicators of fitting effect and result error, constantly adjusting the batch size and the number of network layers. The measured data of the tailings reservoir in Ma'anshan City, China, were used for validation. The results showed that the mean absolute error (MAE) of our long short-term memory–improved empirical mode decomposition–long short-term memory (LSTM-IEMD-LSTM) mode was 0.125, 0.661, and 4.372 lower than that of LSTM, multivariable linear regression–improve empirical mode decomposition–multivariable linear regression (MR-IEMD-MR), and autoregressive integrated moving average model–improve empirical mode decomposition–autoregressive integrated moving average model (ARIMA-IEMD-ARIMA), and the root-mean-square error (RMSE) of our LSTM-IEMD-LSTM compared with LSTM, MR-IEMD-MR, and ARIMA-IEMD-ARIMA decreased by 13.2%, 45.5%, and 84.2%. The model is also used to predict other high-concentration pollutants to test their generalization ability, which also has high prediction accuracy. [ABSTRACT FROM AUTHOR]

Air cargo is an important strategic resource of country and plays an indispensable role in domestic and international trade. Scientific forecasting of air cargo demand is an important basis for airlines to make infrastructure planning and overall investment decisions. Aiming at the uncertainty of air cargo volume data, this paper introduced Bootstrap method for uncertainty estimation and proposed an interval prediction method based on decomposition integration from the practical needs. Specifically, this paper decomposed the historical data by Seasonal and Trend Decomposition using Loess (STL) method firstly, then forecasted the trend and seasonal components by Support Vector Regression (SVR) and Seasonal Autoregressive Integrated Moving Average (SARIMA), respectively. Thirdly, this paper extracted and resampled the white noise component by Bootstrap method. Finally, the prediction results were integrated and reconstructed with the processed white noise to quantify uncertainty using quantile construction intervals. The experimental results of cargo data from two hub airports in China show that the constructed interval can effectively quantify the uncertainty in combination with the predicted results, which provides a novel research idea for probabilistic interval prediction. [ABSTRACT FROM AUTHOR]

Atmospheric visibility is an important indicator that reflects the transparency of the atmosphere and characterizes the air quality, so it is of great significance to study the long-term change in visibility. This paper is based on the global surface summary of day data (GSOD) site dataset and other relevant data, using the Mann–Kendall (MK) mutation point test, wavelet transform, and seasonal autoregressive integrated moving average (SARIMA) model forecasting. The time-frequency domain variation characteristics and related influencing factors of regional visibility in China were studied in detail, and the visibility was predicted; the results of the study showed the following: (1) the overall interannual variation of regional visibility in China has a decreasing trend, and the four-season variation has a decreasing trend, except for the rising trend in summer, with abrupt change points in both the overall interannual variation and the four-season variation. (2) There are main cycles of visibility in the Chinese region with time scales of 180 months and 18 months. Under the time scale of 180 months for the main cycle, the variation period of visibility is about 123 months, experiencing two high to low variations; under the time scale of 18 months for the main cycle, the variation period of visibility is 12 months, experiencing 21 high to low variations. (3) The development of the economy indirectly affects changes in visibility. Cities with high economies are densely populated, with concentrations of various particulate emissions and high concentrations of particulate matter, which can directly reduce visibility. (4) Two prediction models, SARIMA and long and the short-term memory (LSTM) neural network, were used to predict the visibility in China, both of which achieved good evaluation indexes, and the visibility in China may show an increasing trend in the future. [ABSTRACT FROM AUTHOR]

Ozone is a very important trace gas in the atmosphere, it is like a "double-edged sword". Because the ozone in the stratosphere can effectively help the earth's organisms to avoid the sun's ultraviolet radiation damage, the ozone near the ground causes pollution. Therefore, it is essential to explore the time-frequency domain variation characteristics of total column ozone and have a better understanding of its cyclic variation. In this paper, based on the monthly scale dataset of total column ozone (TCO) (September 2002 to February 2023) from Atmospheric Infrared Sounder (AIRS) carried by NASA's Aqua satellite, linear regression, coefficient of variation, Mann-Kendall (M-K) mutation tests, wavelet analysis, and empirical orthogonal function decomposition (EOF) analysis were used to analyze the variation characteristics of the TCO in China from the perspectives of time domain, frequency domain, and spatial characteristics. Finally, this study predicted the future of TCO data based on the seasonal autoregressive integrated moving average (SARIMA) model in the time series algorithm. The results showed the following: (1) From 2003 to 2022, the TCO in China showed a slight downward trend, with an average annual change rate of −0.29 DU/a; the coefficient of variation analysis found that TCO had the smallest intra-year fluctuations in 2008 and the largest intra-year fluctuations in 2005. (2) Using the M-K mutation test, it was found that there was a mutation point in the total amount of column ozone in 2016. (3) Using wavelet analysis to analyze the frequency domain characteristics of the TCO, it was observed that TCO variation in China had a combination of 14-year, 6-year, and 4-year main cycles, where 14 years is the first main cycle with a 10-year cycle and 6 years is the second main cycle with a 4-year cycle. (4) The spatial distribution characteristics of the TCO in China were significantly different in each region, showing a distribution characteristic of being high in the northeast and low in the southwest. (5) Based on the EOF analysis of the TCO in China, it was found that the variance contribution rate of the first mode was as high as 52.85%, and its spatial distribution of eigenvectors showed a "-" distribution. Combined with the trend analysis of the time coefficient, this showed that the TCO in China has declined in the past 20 years. (6) The SARIMA model with the best parameters of (1, 1, 2) × (0, 1, 2, 12) based on the training on the TCO data was used for prediction, and the final model error rate was calculated as 1.34% using the mean absolute percentage error (MAPE) index, indicating a good model fit. [ABSTRACT FROM AUTHOR]

Japanese eels (Anguilla japonica) are a typical migratory fish species with high commercial importance. The Pearl River estuary in southern China is an important natural growing ground for Japanese glass eels, but limited information on Japanese glass eel population characteristics is available, despite their ecological importance. In this paper, we examined the annual patterns of the occurrence of Japanese glass eels in the Pearl River estuary from 2011 to 2022. The most frequently occurring Japanese glass eel's total length is 5.3 cm. The collecting period extended from December to February, and the collection catch-per-unit-effort (CPUE) decreased significantly from 2011 to 2022. The generalized linear model (GLM) indicated that daily changes in Japanese glass eel collection were significantly affected by tidal range, water temperature, and lunar distance. The catch peak appeared when the tidal range rose to 1.7 m, and the water temperature dropped below 8 °C on the full moon days. Overall CPUE analysis showed no significant periodic and inter-annual variability in the period 2011–2022, with the ARIMA model suggesting that the CPUE is expected to remain stable but low in the coming years (2023–2026), although recruitment ultimately depends on the overall spawning stock. [ABSTRACT FROM AUTHOR]

As the scale of distributed renewable energy represented by wind power and photovoltaic continues to expand and load demand gradually changes, the future evolution of the smart distribution network will be directly driven by both distributed generation and user demand. The smart distribution network contains a wide range of flexible resources, and its flexibility and uncertainty will bring great challenges to grid data acquisition and control feedback. To adapt to the precise control and feedback of smart distribution network access equipment under the high proportion of new energy access and to ensure the safe operation of the system, it is urgent to accelerate the study of the evolution of the future distribution grid based on the existing distribution grid. Hence, a multi-stage planning method for distribution networks based on source–load prediction is proposed in this paper. Firstly, a distribution network source–load prediction method based on the autoregressive integrated moving average model (ARIMA) and error gradient sampling is proposed, using ARIMA to predict the scale of source–load development and error gradient sampling based on the generation of source–load scenarios with error intervals. K-means is further used for scenario reduction, to explore multiple operating scenarios of China's distribution network source–load, and the unit's output forecast interval and load demand from 2021 to 2030 for typical regions are derived using rolling forecasts by combining the unit's output, end-demand and clean energy share over the years. Secondly, the planning model of distribution grid evolution in different stages is constructed to analyze the future evolution form of the distribution grid considering the distribution network's load cross-section, respectively, and to provide a development path reference for the future construction of distribution grid form in China. [ABSTRACT FROM AUTHOR]

Solar irradiance data include temporal information and geospatial information, so solar irradiance prediction can be regarded as a spatiotemporal sequence prediction problem. However, at present, most of the research is based on time series prediction models, and the research studies on spatial-temporal series prediction models are relatively few. Therefore, it is necessary to integrate spatial-temporal information to construct a spatial-temporal sequence prediction model for research. In this paper, the spatial-temporal prediction model based on graph convolutional network (GCN) and long short-term memory network (LSTM) was established for short-term solar irradiance prediction. In this model, solar radiation observatories were modeled as undirected graphs, where each node corresponds to an observatory, and a GCN was used to capture spatial correlations between sites. For each node, temporal features were extracted by using a LSTM. In order to evaluate the prediction performance of this model, six solar radiation observatories located in the Xinjiang region of China were selected; together with widely used persistence model seasonal autoregressive integrated moving average and data-driven prediction models such as convolutional neural network, recurrent neural network, and LSTM, comparisons were made under different seasons and weather conditions. The experimental results show that the average root mean square error of the GCN-LSTM model at the six sites is 62.058 W / m 2 , which is reduced by 9.8%, 14.3%, 6.9%, and 3.3%, respectively, compared with other models; the average MAE is 25.376 W / m 2 , which is reduced by 27.7%, 26.5%, 20.1%, and 11%, respectively, compared with other models; the average R2 is 0.943, which is improved by 1.4%, 2.2%, 0.8%, and 0.4%, respectively, compared with other models. [ABSTRACT FROM AUTHOR]

A key element for reducing energy consumption and improving thermal comfort on high-speed rail is controlling air-conditioning temperature. Accurate prediction of air supply temperature is aimed at improving control effects. Existing studies of supply air temperature prediction models are interdisciplinary, involving heat transfer science and computer science, where the problem is defined as time-series prediction. However, the model is widely accepted as a complex model that is nonlinear and dynamic. That makes it difficult for existing statistical and deep learning methods, e.g., autoregressive integrated moving average model (ARIMA), convolutional neural network (CNN), and long short-term memory network (LSTM), to fully capture the interaction between these variables and provide accurate prediction results. Recent studies have shown the potential of the Transformer to increase the prediction capacity. This paper offers an improved temporal fusion transformers (TFT) prediction model for supply air temperature in high-speed train carriages to tackle these challenges, with two improvements: (i) Double-convolutional residual encoder structure based on dilated causal convolution; (ii) Spatio-temporal double-gated structure based on Gated Linear Units. Moreover, this study designs a loss function suitable for general long sequence time-series forecast tasks for temperature forecasting. Empirical simulations using a high-speed rail air-conditioning operation dataset at a specific location in China show that the temperature prediction of the two units using the improved TFT model improves the MAPE by 21.70% and 11.73%, respectively the original model. Furthermore, experiments demonstrate that the model effectively outperforms seven popular methods on time series computing tasks, and the attention of the prediction problem in the time dimension is analyzed. [ABSTRACT FROM AUTHOR]

The South China Sea is China's largest marginal sea area, and it is rich in oil and gas mineral resources; thus, estimating its sea level changes is of practical significance. Based on linear and nonlinear sea level change characteristics, this paper decomposes 1992–2019 monthly mean sea level anomaly time series in the South China Sea into trend, seasonal, and random terms. This paper compares the seasonal autoregressive integrated moving average (SARIMA) and Prophet models for estimating the trend and seasonal terms and the long short-term memory (LSTM) and radial basis function (RBF) models for estimating random terms, and the more suitable models were selected. A Prophet-LSTM combined model was developed based on the accuracy results. This paper uses the combined model to study the effect of known data length on the experimental results and determines the best prediction duration. The results show that the combined model is suitable for short-term and medium-term estimations of 12–36 months. The accuracy at 36 months is 0.962 cm, which proves that the combined model has high application value for estimating sea level changes in the South China Sea. [ABSTRACT FROM AUTHOR]

Different from traditional algorithms and model, machine learning is a systematic and comprehensive application of computer algorithms and statistical models, and it has been widely used in many fields. In the field of finance, machine learning is mainly used to study the future trend of capital market price. In this paper, to predict the time-series data of stock, we applied the traditional models and machine learning models for forecasting the linear and non-linear problem, respectively. First, stock samples that occurred from year 2010 to 2019 at the New York Stock Exchange are collected. Next, the ARIMA (autoregressive integrated moving average model) model and LSTM (long short-term memory) neural network model are applied to train and predict stock price and stock price subcorrelation. Finally, we evaluate the proposed model by several indicators, and the experiment results show that: (1) Stock price and stock price correlation are accurately predicted by the ARIMA model and LSTM model; (2) compared with ARIMA, the LSTM model performance better in prediction; and (3) the ensemble model of ARIMA-LSTM significantly outperforms other benchmark methods. Therefore, our proposed method provides theoretical support and method reference for investors about stock trading in China stock market. [ABSTRACT FROM AUTHOR]

Pu'er tea is a Yunnan geographical indication product, and its brand value ranks first in China. At present, qualitative and quantitative methods with low prediction accuracy are used to predict price. In this paper, based on the current situation and industry characteristics, a differential autoregressive integrated moving average model (ARIMA) is used to predict the short-term price. From the perspective of macro and micro, back-propagation neural network model (BP) was established to predict the long-term price based on the weight ranking of 16 factors affecting the price by technique for order preference by similarity to ideal solution method (TOPSIS). The future price is predicted and analyzed, and then based on the empirical results, suggestions are put forward for the industry in terms of reducing production capacity, increasing consumer demand and combining with the publicity and promotion of Internet. [ABSTRACT FROM AUTHOR]

With Census X-12 model and ARIMA model, this paper quantitatively analyzes the impact of the COVID-19 epidemic on the latent emissiveness of Chinese residents which happened at the end of 2019. The results show that: First, during this epidemic period, the overall latent emissiveness index of Chinese residents decreased by 53.51%, and showed certain spatial difference, and it is not the area with the largest number of confirmed cases, and the area with the most serious epidemic situation, the greater the loss of latent emissiveness. Second, the residents' willingness to travel has a strong fit with the development stage of the epidemic, but there is also a certain lag. In other words, the more severe the epidemic, the lower the willingness of residents to travel; When the epidemic is under control, people's willingness to travel will rise again, but the rise time will be delayed. Third, compared with the period of SARS in 2003, the impact of COVID-19 on China residents' latent emissiveness is greater and more profound, three times as much as that of the SARS period. In view of the serious impact of the epidemic on Chinese residents' willingness to travel, finally, several development suggestions are put forward on how to re-enhance people's travel information and revitalize the tourism industry in the process of normalizing the epidemic prevention and control. [ABSTRACT FROM AUTHOR]

Accurate and reliable prediction of groundwater depth is a critical component in water resources management. In this paper, a new method based on coupling wavelet decomposition method (WA), autoregressive moving average (ARMA) model, and BP neural network (BP) model for groundwater depth forecasting applications was proposed. The relative performance of the proposed coupled model (WA-ARMA-BP) was compared to the regular autoregressive integrated moving average (ARIMA) and BP models for annual average groundwater depth forecasting using leave-one-out cross-validation (LOO-CV). The variables used to develop and validate the models were average groundwater depth data recorded from 1981 to 2010 in Jinghui Canal Irrigation District in the northwest of China. It was found that the WA-ARMA-BP model provided more accurate annual average groundwater depth forecasts compared to the ARIMA and BP models. The results of the study indicate the potential of the WA-ARMA-BP model in forecasting nonstationary time series such as groundwater depth. [ABSTRACT FROM AUTHOR]

Identifying the research topics of China's energy transition policy and predicting future research trends are crucial for policymakers to make informed decisions and advance the energy transition. This study applied the Latent Dirichlet Allocation (LDA) to extract 26 major research topics from the 13,976 abstracts of Chinese policy research papers in web of science (WOS), conceptualized the five most popular topics and used the ARIMA model to predict trends over the next 36 months. The results show that except for Economic and Environmental Impact of Renewable Energy Consumption (Topic13), which will first experience unanticipated fluctuations and then show strong growth capacity, all other topics will continue to remain positive growth trend. Research and Development (Topic3) is driven by the urgency of China's energy transformation. Economic and Environmental Impact of Renewable Energy Consumption (Topic13) is driven by the potential for the development of renewable energy. Economic Impact of Climate Change (Topic26) is driven by the climate change debate. Economic Growth, Urbanization, and Energy Consumption (Topic7) is driven by a discussion of their relationship and the mechanisms influencing them. Regional Efficiency and Productivity Analysis (Topic19) is driven by regional differences in China's energy policies. [ABSTRACT FROM AUTHOR]

Background: Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared. Methods: Autoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Results: By analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence prediction, using ARIMA, SVM and LSTM. And we obtained 22.25, 20.0368, 11.75 for cases number prediction, using three models. For MAPE metrics, the results were 23.5%, 21.7%, 15.08%, and 23.6%, 21.44%, 13.6%, for incidence prediction and cases number prediction, respectively. For MAE metrics, the results were 0.018, 0.0167, 0.011 and 18.003, 16.5815, 9.984, for incidence prediction and cases number prediction, respectively. Conclusions: Comparing ARIMA, SVM and LSTM, we found that nonlinear models(SVM, LSTM) outperform linear models(ARIMA). LSTM obtained the best performance in all three metrics of RSME, MAPE, MAE. Hence, LSTM is the most suitable for predicting hepatitis E monthly incidence and cases number. [ABSTRACT FROM AUTHOR]

This case study uses a long short-term memory (LSTM)–based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM-based model is compared with the autoregressive integrated moving average (ARIMA) model, the support vector regression (SVR) model, and the random forests (RF) model based on data with time resolutions ranging from 15 min to 24 h. Additionally, this paper investigates the performance of the LSTM-based model in predicting multiple successive data points. Results show that the LSTM-based model can offer predictions with improved accuracy than the other models when dealing with data with high time resolutions, data points with abrupt changes, and data with a relatively high uncertainty level. It is also observed that the LSTM-based model exhibits the best performance in predicting multiple successive water demands with high time resolutions. In addition, the inclusion of external parameters (e.g., temperature) cannot enhance the performance of the LSTM-based model, but it can improve ARIMAX's prediction ability (ARIMAX is the ARIMA with variables). These observations provide additional and improved evaluations regarding the LSTM-based models used for short-term urban water demand forecasting, thereby enabling their wider adoption in practical applications. [ABSTRACT FROM AUTHOR]

Drought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of theirminimal information requirements. Themotivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA-support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA-SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash-Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA-SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA-SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1-2 months ahead show reasonably good agreement with the actual data. [ABSTRACT FROM AUTHOR]

Structural changes often occur in air passengers due to some external factors such as airport expansion, policy orientation and economic development; model uncertainty is a common long-standing issue in forecasting. To address these issues, a novel time-varying Jackknife model averaging method (TVJMA) (Sun et al, 2020, 2012) is employed to predict air passengers of the Top 5 airports in China. Based on nonparametric estimation, the optimal time-varying weights for various candidate models with time-varying parameters in candidate models are obtained by minimizing the local Jackknife criterion at every time point t. TVJMA method allows the weights and parameters to change over time. Empirical results show that the TVJMA method used in this paper is significantly superior to other benchmark models, including Hansen and Racine's (2012) Jackknife model averaging method (JMA), autoregression model (AR), autoregression integrated moving average model (ARIMA), seasonal autoregression integrated moving average model (SARIMA), and time-varying parameter model (TVP). Furthermore, the predictive effect of TVJMA is robust to different test sets and prediction steps. Overall, TVJMA method effectively reduces the predictive risk caused by structural changes and model uncertainty, and thus produces accurate and stable forecasts of air passengers. [ABSTRACT FROM AUTHOR]

• Introduce the state-of-art temporal fusion transformers (TFT) into economic system forecasting based on the high compatibility between deep learning and the nonlinear characteristics of socio-economic systems. • Propose a scientific economic system forecasting framework on the foundation of the extended gross final product (GFP). • Build a four-dimensional evaluation system including numerical error, direction accuracy, timing error and turning point accuracy. • Empirically forecast the monthly output indicators of the Chinese macroeconomic system. • TFT results have obvious advantages over benchmark models including ARIMA, BP, LSTM, TCN and N-BEATS through comparison analysis. Although helpful in reducing the uncertainty associated with economic activities, economic forecasting often suffers from low accuracy. Recognizing the high compatibility between deep learning and the nonlinear characteristics of socioeconomic systems, in this paper, we introduce state-of-the-art temporal fusion transformers (TFTs) into the field of economic system forecasting and predict the performance of the Chinese macroeconomic system. Based on an extended analysis of gross final product (GFP) and the intertemporal dynamic relationship between demand-side indicators and output indicators, we establish a scientific economic forecasting framework. To summarize the forecasting characteristics of the TFT algorithm, we compare its one-step and three-step modeling effects in forecasting output indicators with a series of representative benchmark models. According to our proposed four-dimensional evaluation system, the forecasts for China's macroeconomic system provided by the TFT model have obvious advantages in terms of overall stability, forecasting efficiency, reduction of numerical and timing errors, direction accuracy, and turning point accuracy. The forecast results show that China's economy faces a risk of slowing growth in the post-pandemic period. [ABSTRACT FROM AUTHOR]

Based on five original sentiment indicators in Hong Kong stock market, including the closed-end fund discount rate, the average first-day returns of initial public offering (IPO), the number of IPO, the market trading volume and turnover, this paper uses the principal component analysis method to calculate a comprehensive investor sentiment index. After excluding the trend and seasonal factors from the comprehensive investor sentiment index and Hong Kong Hang Seng Index by autoregressive moving average-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) family models, this paper makes correlation analysis and Granger causality test on the residuals of ARMA-GARCH model. The study shows that ARMA-GARCH models can effectively process the autocorrelation and heteroscedasticity of investor sentiment index and Hong Kong Hang Seng Index. Also, it indicates that the Hang Seng index return is the Granger cause of investor sentiment in short terms while not in long terms. That is to say investor sentiment would be more optimistic in short terms when the Hong Kong stock market goes up, and vice versa. [ABSTRACT FROM AUTHOR]

Accurate and robust fine-grained expressway traffic volume simulation is a critical issue in intelligent transportation systems and real-time traffic-related applications. However, the fine-grained expressway traffic volumes aggregated from vehicle trajectories heavily rely on individual heterogeneity, making it challenging for modeling and accurate simulation. In order to eliminate the influence of individual heterogeneity on the modeling and simulation of the fine-grained expressway traffic volume, this paper proposes a novel method named Quantum Harmonic Oscillator Model for Fine-grained Expressway Traffic Volume Simulation (FGTVS-QHO). FGTVS-QHO adopts the wave function of the quantum harmonic oscillator model to describe the irregular evolution of the location of the vehicle. Then several optimization strategies are applied to the numerical solution of the wave function. FGTVS-QHO is validated with the expressway traffic volumes of six exits along the Nanjing-Shanghai Expressway in China. The simulation performance of FGTVS-QHO is compared with the methods of Long and Short-Term Memory (LSTM) networks and Autoregressive Integrated Moving Average (ARIMA). FGTVS-QHO shows higher simulation accuracy, less time cost, and lower parameter complexity. Especially, the average simulation accuracy of FGTVS-QHO is improved by 24.80% and 58.05% compared with the above two methods, respectively. The scale effects of FGTVS-QHO also indicate that it is adaptive to simulate the expressway traffic volume at fine time granularity. This paper provides a new potential method for fine-grained expressway traffic volume simulation with strong individual heterogeneity. • A traffic volume simulation model considering individual heterogeneity is proposed. • The irregular evolution of the location of vehicle is expressed by a wave function. • Our model can well simulate the complex fluctuation of fine-grained traffic volume. • Our model has the potential to model traffic with unobservable driving behavior. [ABSTRACT FROM AUTHOR]

Abstract: China is currently facing the threat of serious air pollution and has adopted a series of governance policies. However, the fact that PM 2.5 indicator in some regions often reached far higher than the official threshold since 2015 has reflected that China's current Air Quality Index system for measuring and evaluating air quality is still not perfect. Therefore, this paper extended the current air quality indicator system officially used by China. Using improved Entropy-weighted Factor Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Method, we calculated the air quality scores of 26 cities in China's most economically developed region - the Yangtze River Delta region based on their daily average concentration numbers of main air pollutants from February 2015 to January 2018. Based on the air quality calculation result, we further studied the effectiveness of air pollution control policies of this region by using the Box-Jenkins Model with fuzzy strategy parameter adjustment. Our findings are: the pollution control policy is more effective to Shanghai, Jiangsu and Zhejiang. The air quality score of Shanghai has improved by 51.07% compared with its level before the policy. Meanwhile, the air quality improvement in Zhejiang province has shown the characteristics of "Campaign-style Governance" right before the G20 Summit, especially that in Hangzhou, the host city of the Summit. On the other hand, maybe due to late start of air pollution control programs, the cities of Anhui province haven't shown obvious improvements in air quality, and some cities have even seen deterioration in air quality scores during our study period. Graphical abstract Image 1 Highlights • We extended the current air quality indicator system officially used by China. • We used the Entropy-weighted Factor Method and TOPSIS Method for assessing. • We studied the control policies by Box-Jenkins Model with fuzzy strategy parameter. • Results and corresponding reasons were analyzed according to provinces and cities. • Policy recommendations for air pollution control in YRD were proposed. [ABSTRACT FROM AUTHOR]

Abstract China is the world's largest net importer of oil and the second largest oil consumer; consequently, changes of China's foreign oil dependence significantly impact both the Chinese and the international oil market. To enhance the forecasting ability of China's foreign oil dependence, this study combines the nonlinear metabolic grey model (NMGM) with the linear autoregressive integrated moving average model (ARIMA), thus obtaining the combined NMGM-ARIMA model. The proposed technique uses the linear ARIMA to correct NMGM forecasting residuals, thus improving forecasting accuracy. The proposed technique achieves a mean absolute error of 2.1–2.3%, reflecting its high reliability. The proposed NMGM-ARIMA was used to forecast China's foreign oil dependence for the period of 2017–2030 from two dimensions. For the first dimension, the gap between China's oil demand and supply was forecast. To fill this gap, China has to import oil; therefore, this gap is responsible for China's foreign oil dependence. For the second dimension, the change of China's foreign oil dependence level was directly forecast. Both dimensions indicate a similar conclusion, namely that the Chinese foreign oil dependence level will increase from 65% in 2016 to over 80% in 2030. A high level of 80% dependence on foreign oil would bring major concern to China. The policy recommendations given at the end of the paper will help China's decision makers respond appropriately. Highlights • The proposed forecasting technique uses the linear ARIMA to correct NMGM residuals. • The mean absolute percent error of the proposed technique is 2.1–2.3%. • Forecasting China's oil gap between production and consumption for 2017–2030. • Forecasting China's foreign oil dependence for 2017–2030. • Two-dimensional forecasting results show China's foreign oil dependence will exceed 80% by 2030. [ABSTRACT FROM AUTHOR]

As a significant indicator to reflect the tourism demand of residents, the variation trends of the travel rate which predicted accurately and scientifically will provide reference frames for macro policies to guide the practice of tourism supply-side structural reform. The ARIMA models were established in this paper, and with the help of the data of tourism rate in 2014-2015, the predicable accuracy of these models was verified and passed. On this basis, the data of the urban residents’ travel rate in China from 2016 to 2020 was predicted. The empirical results show that the predicable precision of ARIMA model(2.1.2) is higher, which can be used as an effective basis for predicting the tendency of domestic urban residents’ future travel rate. [ABSTRACT FROM AUTHOR]

This paper investigated the impacts of global crude oil price shocks on China's precious metals market using the meliorated autoregressive conditional jump intensity (ARJI) model (Chan and Maheu, 2002) and the extended autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) (Gronwald, 2012), based on the comparative analysis of gold and platinum markets. The results showed that discrete jumps existed in the crude oil market. Considering the volatility clustering of oil returns, the effect of oil price expected shocks on precious metals market was negative (−0.7025,-1.3341), due to the profitability of capital; While that of unexpected shocks was positive (0.0211,0.0645), which can be explained by the adaptive expectation theory. In terms of discrete jumps, the impact of current jumps on precious metals market was significantly negative (−1.4810,-2.6775), according to the risk transmission theory, and the responses tended to be characterized by “overreactions”. In addition, some evidence also suggested permanent volatility effects in China's precious metals market. [ABSTRACT FROM AUTHOR]

• A grey seasonal trend support vector machine for energy forecasting is proposed. • The model can efficiently identify the seasonal fluctuations in energy data. • The model is powerful to predict China's energy production. • The predictive performance of the new model is much superior to the benchmarks. This paper proposed a hybrid ensemble forecasting technique that incorporates the merits of the accumulation generation operation (AGO), least-square support vector regression (LSSVR), dummy variable, and time trend item to forecast the seasonal time series characterized by nonlinearity and uncertainty. This proposed framework, a grey seasonal trend least squares support vector machine (GSTLSSVR), can make it easy to add arbitrary seasonality to any feature, which improves model realism significantly. Furthermore, the grid search method and the last block validation are developed to identify the optimal hyperparameter, making it possible for the developed method to capture seasonal and nonlinear patterns of the original data. For illustration and verification, the quarterly petroleum coke production and monthly crude oil production in China are employed to construct diverse competing models, including the seasonal fluctuation grey model (SGM(1,1)), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters (HW), backpropagation neural network (BPNN), a grey seasonal least squares support vector machine (GSLSSVR), long short term memory (LSTM) and LSSVR. The forecasting values of quarterly petroleum coke production are 668.74, 682.68, 679.99, 704.35 for 2019 and 717.59, 734.99, 727.16, 741.53 for 2020, with the lowest MAPEP value (3.7566%). Moreover, the monthly predicted crude oil production for 2019 are 3113.9, 1645, 1562.2, 1625.4, 1599.3, 1619.1, 1627, 1576.9, 1629.6, 1583.6, and 1615.6, while those counterparts for 2020 are 3093.2, 1625, 1547.5, 1621.5, 1610.6, 1644.9, 1662.9, 1615.4, 1667.4, 1615, 1667.4, 1615, and 1640.2, with the lowest MAPEP value (1.0034%). Generally, the empirical results show that the proposed model obtains superior overall performance to other benchmarks in both case studies. Therefore, this proposed model exhibits promising prospects for identifying seasonal patterns in energy production sequences. [ABSTRACT FROM AUTHOR]

Background: Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration.Methods: We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead.Results: The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better.Conclusions: Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step. [ABSTRACT FROM AUTHOR]

• We consider some indicators that have not been considered in previous studies, such as the impact of historical and social factors, which are very important and necessary in measuring national space exploration capabilities. • We introduce a more reasonable quantity (Equity Evaluation Index) to measure global equity, which is more objective for both stronger and weaker countries in space exploration. • The optimization model takes different aspects of the space industry into consideration (e.g., funding, technology, scientific research), providing suggestions for countries at different developing levels to take advantage of its strengths and cover its weaknesses. In this paper we introduce an equity evaluation index (EEI) to measure the global equity on asteroid mining, and present a global equity evaluation model by applying the analytic hierarchy process (AHP), entropy weight method and the technique for order preference by similarity to ideal solution (TOPSIS). Based on multi-objective optimization, we provide a global cooperative decision model to allocate the inputs and outputs among countries based on their strengths and demands. Furthermore, we analyze the the trends of EEI of each country under the influence of the outbreak events by using the autoregressive integrated moving average model. Our results show that among all countries with foundations in aerospace, China has the highest EEI while Russia ranks the lowest. Humans mining minerals in a cooperative manner will achieve a win-win situation. [ABSTRACT FROM AUTHOR]

• A novel multivariate probabilistic forecasting method. • The impacts of probabilistic forecasting's performance on hydro-wind dispatch. • Appropriate trade-off between running time and benefit. • Case study that optimizes the long-term optimal dispatch scheme. There are two difficulties in long-term optimal dispatch of hydro-wind hybrid systems. First, monthly runoffs and wind speeds have the dynamic characteristics such as variability, instability, seasonality, heteroscedasticity, linear and nonlinear dynamic correlations. Second, hydro-wind hybrid systems have highly non-convex nonlinear constraints. To overcome the problem, this research develops a novel X-12 seasonal adjustment, vector autoregressive integrated moving average (VARIMA), component generalized autoregressive conditional heteroscedasticity (C-GARCH) and dynamic copula mixed model to estimate the joint probability distribution of runoffs and wind speeds. And then, this paper builds a multistage stochastic mixed-integer linear programming (MILP) with the help of several linearization methods. Finally, the paper compares several probabilistic forecasting models' performances and analyzes their impacts on the dispatch of the hydro-wind hybrid system under different hydrological years. A hydro-wind hybrid system in southwest China is taken as an example. The case study leads to the following conclusions: 1) the more sufficient to capture the dynamic characteristics of variables, the higher benefit will be; 2) it is necessary to increase the scale of scenario tree to reduce the electricity shortfall during the dry year; 3) serious spilled water can be caused by insufficient interregional transmission capacity under the wet year and it is the most appropriate to expand the capacity to 8000 MW; 4) the model proposed in this paper can increase the economic benefit by 0.466 × 10 9 C N Y , 1.775 × 10 9 C N Y and 0.400 × 10 9 C N Y during the normal, dry and wet year, respectively. [ABSTRACT FROM AUTHOR]

China has set out an ambitious target of emission abatement; that is, a 60–65% reduction in CO2 emission intensity by 2030 compared with the 2005 baseline level and emission peak realisation. This paper aimed to forecast whether China can fulfil the reduction target of CO2 emission intensity and peak by 2030 based on the historical time series data from 1990 to 2018. Four different forecasting techniques were used to improve the accuracy of the forecasting results: the autoregressive integrated moving average (ARIMA) model and three grey system-based models, including the traditional grey model (1,1), the discrete grey model (DGM) and the rolling DGM. The behaviours of these techniques were compared and validated in the forecasting comparisons. The forecasting performance of the four forecasting models was good considering the minimum mean absolute percentage error (MAPE), demonstrating MAPE values lower than 2%. ARIMA showed the best forecasting performance over the historical period with a MAPE value of 0.60%. The forecasting results of ARIMA indicate that China would not achieve sufficient reductions despite its projected emission peak of 96.3 hundred million tons by 2021. That is, the CO2 emission intensity of China will be reduced by 57.65% in 2030 compared with the 2005 levels. This reduction is lower than the government goal of 60–65%. This paper presents pragmatic recommendations for effective emission intensity reduction to ensure the achievements of the claimed policy goals. [ABSTRACT FROM AUTHOR]

This paper aims to investigate how the recent overheating economy and other economic factors influence the manufacturing industry in China. It makes use of Chinese historical data from 1950 to 2007 provided in the China Statistical Databases authorized by the National Bureau of Statistics of China for the development of econometric models. Regression analysis is used to investigate and verify the importance of and relationships among the variables, while exponential smoothing and autoregressive integrated moving average methods are applied for forecasting the important variables. This study provides empirical evidence to identify the main impact factors on the growth of the manufacturing industry, thereby enabling firms to take them into account when planning their manufacturing strategy. It also enables firms to get an insight into the development of China’s manufacturing industry in this dynamic, competitive economy. [ABSTRACT FROM AUTHOR]

Statistical and structural characteristics provide us with a multi-dimensional picture of time series. Rooting the properties in a unified scheme is the preliminary step to develop a model that can reproduce most part or even the whole of the picture. In this paper, we proposed a concept called multi-scale transition matrix, which is a series of transition matrices describing the jumping probabilities between states after specified numbers of jumps each. The eigenvectors corresponding to the unitary eigenvalues are identical with the probability distribution function. The second largest eigenvalues depict the upper-boundary curve of the persistence. The change speed of the eigenvector corresponding to the second largest eigenvalue along with time scale displays the relaxation behavior of the time series. The multi-scale matrix maintains the structure of autocorrelation in the original time series and its evolution, which are merged by the averaging procedure in the statistical properties. These predictions are confirmed by using the series generated with the Auto-Regressive Conditional Heteroskedasticity Model, the Auto-Regression Model and the fractional Brownian motion, and the empirical records for Shenzhen Component index in Mainland China and the word length series of the novel entitled Remembrance of Things Past written by Marcel Proust. Hence, the concept is a good candidate of bridges between the multi-dimensional picture and the dynamical models of the time series. [ABSTRACT FROM AUTHOR]

Abstract: This paper compares the predictive performance of ARIMA, artificial neural network and the linear combination models for forecasting wheat price in Chinese market. Empirical results show that the combined model can improve the forecasting performance significantly in contrast with its counterparts in terms of the error evaluation measurements. However, as far as turning points and profit criterions are concerned, the ANN model is best as well as at capturing a significant number of turning points. The results are conflicting when implementing dissimilar forecasting criteria (the quantitative and the turning points measurements) to evaluate the performance of three models. The ANN model is overall the best model, and can be used as an alternative method to model Chinese future food grain price. [Copyright &y& Elsevier]

• EMD is applied to reduce the complexity of the prediction subsequence. • The SE of the decomposed sub-sequence is induced to improve the prediction precision of wind speed. • LSTM is applied to predict the high frequency sub-sequences. • The ARIMA is employed to predict the low frequency sub-sequences and one residual. • A hybrid EMD-LSTM-ARIMA model is successfully proposed for wind speed prediction. Wind speed is the key factor of wind power generation. With the increase of the proportion of wind power generation in total power generation, the accurate prediction of wind speeds plays an important role in the stable operations of power grids. However, the strong randomness of wind speeds makes it difficult to accurately predict wind speeds. Thus, a wind speed prediction model combining empirical mode decomposition (EMD) with some novel recurrent neural networks (RNN) and the autoregressive integrated moving average (ARIMA) is proposed to solve the problem. The selected RNNs are long short-term memory network (LSTM) and the gated recurrent unit (GRU) network. In this model, EMD is used to decompose the wind speed sequence to reduce the complexity and non-stationary of the series. The entropy of the samples of the sub-sequences after decomposition is calculated. Consequently, LSTM is applied to predict the high frequency sub-sequences with large entropy while the ARIMA is employed to predict the remaining low frequency sub-sequences and one residual. Finally, the prediction results of each sub series are combined to obtain the final prediction results. To verify the accuracy and stability of the model, four wind speed data sets form Inner Mongolia, China, are used to test the proposed methods. Five models are established in four practical cases and their performances are compared with the performances of the proposed model. The results in this paper show the following: (1) the EMD method can improve the wind speed prediction performance when it is combined with LSTM; (2) after decomposition, LSTM is suitable for predicting high complexity subsequences and the ARIMA is suitable for effectively predicting low complexity subsequences based on the different sample entropies; and (3) the root mean squared errors (RMSEs) of the hybrid model on the four wind speed data sets are 0.4163, 0.2085, 0.1613, and 0.2790, respectively, which are basically lower than those of the five models compared. Therefore, it is feasible to apply the hybrid model to wind speed prediction. [ABSTRACT FROM AUTHOR]

Developing more effective carbon mitigation strategies requires accurate forecasting of carbon emissions. This is especially true for China, the United States and India, which produce over half of the world's carbon emissions. China, the United States, and India represent three different types of carbon emissions data. The first data type is typical for China: rapid grows which is thereafter slowing down. The US carbon emissions data show volatile growth and decline. The third data type is common for India's data: accelerated growth. To deal with the volatility of the data and to improve the forecasting accuracy, this paper combines Metabolic Nonlinear Grey Model (MNGM) with Autoregressive Integrated Moving Average (ARIMA) to develop the combined MNGM-ARIMA model, and MNGM with Back Propagation Neural Network (BPNN) to develop the new combined MNGM-BPNN model. In this way, ARIMA and BPNN could correct the modelling residual error of MNGM, to decrease the forecasting error. Through the quantitative data study of the countries in focus, we have made the following findings: First, the mean relative percent errors of the proposed MNGM-ARIMA and MNGM-BPNN models are 1.35% and 1.57%, which are lower than the traditional MNGM, ARIMA, and BPNN models. Besides, it is projected that during the period of 2019–2030, the US′ carbon emissions will keep a downward trend, while carbon emissions in China and India will continue to grow. Finally yet importantly, in the future periods of time, the carbon emissions growth rate in India will be faster than that for China. The above findings have led us to the following conclusions: (1) Error values of those two proposed approaches have proven that the strategy of correcting the forecasting error of the previous models with the latter models can effectively improve the forecasting accuracy. (2) The proposed two combined forecasting techniques have shown sound performance for the three types of data. It is reasonable to believe that new approaches will help to improve insight into better forecasting carbon emissions in other countries and regions of the world. (3) China and India will remain the major sources of global carbon emissions, and should therefore continue their efforts on carbon emission reduction. Image 10221 • China, the United States and India contribute half of global carbon emissions. • Forecasting the carbon emissions is conducive to global carbon emission reduction. • Strategy of "error correction + quadratic modeling" and new models are proposed. • Mean relative errors of the two proposed models achieved 1.35% and 1.57%. • Future carbon emissions growth will be concentrated in developing countries. [ABSTRACT FROM AUTHOR]

Wind energy is crucial renewable and sustainable resource, which plays a major role in the energy mix in many countries around the world. Accurately forecasting the wind energy is not only important but also challenging in order to schedule the wind power generation and to ensure the security of wind-power integration. In this paper, four kinds of hybrid models based on cyclic exponential adjustment, adaptive coefficient methods and the cuckoo search algorithm are proposed to forecast the wind speed on large-scale wind farms in China. To verify the developed hybrid models' effectiveness, wind-speed data from four sites of Xinjiang Uygur Autonomous Region located in northwest China are collected and analyzed. Multiple criteria are used to quantitatively evaluate the forecasting results. Simulation results indicate that (1) the proposed four hybrid models achieve desirable forecasting accuracy and outperform traditional back-propagating neural network, autoregressive integrated moving average as well as single adaptive coefficient methods, and (2) the parameters of hybrid models optimized by artificial intelligence contribute to higher forecasting accuracy compared with predetermined parameters. [ABSTRACT FROM AUTHOR]