Long-term Forecasting Model Research Articles

Analyzing the Impact of Causal Factors on Political Management to Determine Sustainability Policy under Environmental Law: Enriching the Covariance-based SEMxi Model

This research aims to develop a causal relationship model on political management for sustainability policy formation under Thai environmental law by applying the best and valid model with a non-spurious property called the Covariance-based on Structural Equation Model with exogenous variables (Covariance-based SEMxi Model). This newly-developed model is in distinction with any past models as it is made effectively applicable to any sectors across areas. The model can also be utilized to design a long-term forecasting model with the ability to determine appropriate future scenarios. When assessing the covariance-based SEMxi model performance, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) are estimated at 1.19% and 1.30%, respectively, in comparison of other models, including Gray-Autoregressive Integrated Moving Average Model (GM-ARIMA), Gray Model (GM), Back Propagation Neural Network (BP), Artificial Neural Natural Model (ANN), and Multiple Regression Model (MR). As for the results, this research reveals a direct impact of economic factors on environmental and social factors. In the meanwhile, social factors have a direct impact on environmental and economic factors. The research also indicates a direct effect on the environment with a maximal magnitude of 67%. Whereas a direct effect of social factors on the environment is detected at the magnitude of 55%. These effects are perceived to exceed the specified carrying capacity set by Thailand. In addition, a causal relationship is observed between economic and social factors, where the environment is found with the lowest error correction capability of only 5 percent. At the same time, economic and social factors are noticed with greater correction capability of 59% and 31%, respectively. This finding implies that the ecosystem will experience slow recovery whenever it deteriorates. Hence, the government must place a higher concentration on the environment, while different measures on environmental legislation should be closely controlled to contain any future damage. Besides, energy consumption must be managed not to exceed the established carrying capacity by simultaneously implementing both proactive and reactive measures. This process can be strengthened by optimizing the newly-introduced model produced by this work for a scenario design in policy management to attain sustainability.

Long Term Electricity Load Forecast Based on Machine Learning for Cameroon’s Power System

A reliable power supply has long been identified as an important economic growth parameter. Electricity load forecasts predict the future behavior of the electricity load. Carrying out a forecast is important for real-time dispatching of power, grid maintenance scheduling, grid expansion planning, and generation planning depending on the forecasting horizon. Most of the methods used in long-term load forecasting are regressions and are limited to predicting peak loads of a yearly or monthly resolution with low accuracy. In this paper, we propose a method based on long short-term memory-recurrent neural networks (LSTM-RNN) cells with relations between identified influential econometric load-driving parameters which includes: the Gross Domestic Product (GDP), Population (H), and past Electric Load Data. To the best of our knowledge, the use of the GDP and H as two additional independent variables in load forecast modelling using machine learning techniques is a novelty in Cameroon. A comparison was performed between a linear regression (LR)-based long-term load forecast model (a model currently used by the Transmission System Operator of Cameroon) and LSTM-RNNs model constructed. The results generated were evaluated using a Mean Absolute Percentage Error (MAPE) within the same period of evaluation, and the overall value of the MAPE obtained for LSTM-RNNs model was 5.4962 whereas that for the LR model was 7.5422. Based on these results, the LSTM-RNN model is considered highly accurate and competent. The model was used to generate a forecast for the period of 2022–2026 with an hourly resolution. A MAPE of 5.4962 was obtained with a computational time of approximately ten minutes, making the model vital for offline use by utilities due to its capacity to quantitatively and accurately predict long-term load with an hourly resolution.

GA-GRGAT: A novel deep learning model for high-speed train axle temperature long term forecasting

Long-term axle temperature prediction plays a significant role in train condition assessment and daily maintenance. However, most of methods make predictions for short-term conditions. In this paper, a method named GA-GRGAT is introduced that uses GAT and GAN to forecast the long-term axle temperature. In the proposed method, a GRGAT framework is used as a spatiotemporal fusion in temperature prediction. The GAN network with the GRGAT framework is used to construct a temporal conditional sequence after analyzing the periodic variation of the axle temperature, which can fuse the historical axle temperature information to improve the long-term prediction accuracy of the GA-GRGAT model. Our method in Python software and using the actual high-speed trains datasets in spring and summer. We using MAE, RMSE, MAPE, PCC to evaluate the accuracy of prediction. The accuracy of the GA-GRGAT is more than 90% on long-term predictions (1 day), and more than 80% on super long-term predictions (2 week). The GA-GRGAT method outperforms and is more accurate than the classical forecasting methods such as GRU, GOAMLP, DCNN, SVR and HA. In addition, the cost time of the proposed method is less than 5 min, which meets the requirements of high accuracy and long time.

Short-Term and Long-Term COVID-19 Pandemic Forecasting Revisited with the Emergence of OMICRON Variant in Jordan.

Three simple approaches to forecast the COVID-19 epidemic in Jordan were previously proposed by Hussein, et al.: a short-term forecast (STF) based on a linear forecast model with a learning database on the reported cases in the previous 5–40 days, a long-term forecast (LTF) based on a mathematical formula that describes the COVID-19 pandemic situation, and a hybrid forecast (HF), which merges the STF and the LTF models. With the emergence of the OMICRON variant, the LTF failed to forecast the pandemic due to vital reasons related to the infection rate and the speed of the OMICRON variant, which is faster than the previous variants. However, the STF remained suitable for the sudden changes in epi curves because these simple models learn for the previous data of reported cases. In this study, we revisited these models by introducing a simple modification for the LTF and the HF model in order to better forecast the COVID-19 pandemic by considering the OMICRON variant. As another approach, we also tested a time-delay neural network (TDNN) to model the dataset. Interestingly, the new modification was to reuse the same function previously used in the LTF model after changing some parameters related to shift and time-lag. Surprisingly, the mathematical function type was still valid, suggesting this is the best one to be used for such pandemic situations of the same virus family. The TDNN was data-driven, and it was robust and successful in capturing the sudden change in +qPCR cases before and after of emergence of the OMICRON variant.

Подходы к построению и использованию модели долгосрочного бюджетного прогноза на региональном уровне

In compliance with the requirements of the budget legislation the constituent entities of the Russian Federation work out and adopt long-term budget forecasts. The major drawbacks of the current regional forecasts are inadequate methodology of calculating indicators (revenues, expenditures, deficit, debt) and an indistinct role of forecasting in the budgeting process. The article gives specific recommendations on assessing the parameters of revenues and expenditures of the regional budget for a long term horizon. The author focuses on the relationship of the long-term budget forecasts with the other documents of strategic planning (notably, forecasts of socio-economic development). The author suggests various formulas for calculating revenues and expenditures of regional budgets in the long term, as well as options for detailed elaboration of these parameters depending on the availability of background information and the peculiar features of the region. The article also formulates recommendations on the formalized approaches to assess the long-term consequences of the region’s budget policy, as well as to quantify external challenges on a long-term budget forecast model. Approbation of the proposed approaches was carried out on the example of a simulation model based on the data of the Khanty-Mansiysk Autonomous Okrug.

Волны тепла и блокирующие антициклоны летом 2021 года в прогнозах и наблюдениях

The main features of the temperature regime and atmospheric circulation in the Northern Hemisphere in the summer of 2021 are considered based on the ERA5 reanalysis (ECMWF) of surface air temperature and 500 hPa geopotential height with a daily resolution. Using the operational version of the SL-AV semi-Lagrangian atmosphere model for long-term forecasting, the potential of forecasting heat and cold waves, as well as the Extreme Forecast Index (EFI) against a background of quasi-stable circulation modes of the blocking type are determined. The dependence of the forecast skill on the phase of development of the blocking anticyclone (initiation, mature phase, dissipation) is shown. The results can be used to make forecasts of extreme weather events on intraseasonal timescales. Keywords: extreme weather events, heat and cold waves, blocking anticyclones, long-term weather forecasts

Towards a Novel Efficient Model for Long-Term Traffic Flow Forecasting: Spatial-Temporal Masked Transformer Networks with Informer

Towards a Novel Efficient Model for Long-Term Traffic Flow Forecasting: Spatial-Temporal Masked Transformer Networks with Informer

Collaborative Forecasting and Analysis of Fish Catch in Hokkaido From Multiple Scales by Using Neural Network and ARIMA Model

Fishery catch forecasting is a crucial aspect of aquatic research because of its relevance to establishing effective fishery management and resource allocation systems. In this study, we aim to forecast and analyze fish catch by collaboratively processing data using methods at multiple scales. To this end, we propose two computational fishery catch forecasting models. A neural network model based on the multi-timescale features of a convolutional neural network and a long short-term memory neural network is proposed and implemented to forecast short-term measures for the daily catch in the eastern ports of Hokkaido, Japan. Similarly, we propose a long-term catch forecasting and analysis model combining the autoregressive integrated moving average (ARIMA) method and a neural network to explore short-term water temperature and long-term catch dependence in the case of sparse data; we implement this method to investigate the total monthly catch in Hokkaido. The experimental results demonstrate that the proposed methods were able to effectively forecast and analyze fishery catch based on different data scales, volumes, and other complex situations. This is also the first work in the field that considers multiple perspectives.

Linear dynamic fuzzy granule based long-term forecasting model of interval-valued time series

The studies on modeling and analysis of time series based on fuzzy granulation have shown that fuzzy granulation is an effective approach in data mining of time series. However, the investigation of fuzzy granulation of interval-valued time series (ITS) and its applications has just begun in recent years with appearance of few research results. Different from the existing studies, this paper carried out the investigation of fuzzy granulation of ITS in interval number space instead of real number space. Two distinguished concepts, namely static fuzzy information granules and dynamic fuzzy information granules of ITS, are proposed firstly. Then the approaches for constructing a static fuzzy information granule and a dynamic fuzzy information granule are designed respectively. After that, two fuzzy granulation methods for ITS based on the above two approaches are presented in the framework of interval analysis under the guidance of the principle of justifiable granularity. Based on the specific proposed fuzzy granule which is called linear dynamic fuzzy granule, a long-term forecasting model for ITS was developed with the aid of artificial neural network. Experiments conducted on several ITS from stock markets with different dynamic characteristics showed the outperformance of the proposed long-term forecasting model.

A novel ensemble model for long-term forecasting of wind and hydro power generation

Power generation scenario modelling has become an integral part of long-term planning in power system due to high penetration of variable renewable energy. It requires accurate estimates of power generation from different resources to find cost-optimal mix of generations. Predicting the generation of weather dependent renewables in long-term is not feasible but an adaptive long-term forecasting model based on univariate time-series analysis can provide the solution. Therefore, an effort has been made through this paper to provide accurate medium to long-term forecasts (a week-ahead to a year-ahead) for wind and hydro power generation using a novel ensemble forecasting model. The proposed model is devised in three phases; Phase-I develops a hybrid model using ARIMA (Auto Regressive Integrated Moving Average) and Bi-LSTM (Bidirectional Long Short Term Memory) predictions. Phase-II integrates the forecasts of seasonal and off-season generation periods obtained via a Diligent Search Algorithm (DSA). DSA is an innovative algorithm, designed to identify the hidden seasonalities that are responsible for the intermittent behaviour of wind and hydro power generation time-series. Finally, Phase-III facilitates amalgamation of prediction results of Phase-I and Phase-II to build the proposed forecasting model. Results show that MAE (Mean Absolute Error) for wind and hydro power are 1.97% to 5.52% and 2.3% to 6.42% while RMSE (Root Mean Square Error) varies from 2.79% to 7.8% and 2.63% to 8.4% respectively in a week-ahead to a year-ahead scenarios. Since this model is specifically designed for a year-ahead forecasting scenario, its performance can become unstable beyond this time horizon.

Исследование изменений климатических норм в долгосрочном прогнозе ледовитости Охотского моря

There is a similarity in dynamics and a quantitative difference in the ice cover regime in four consecutive 30-year periods: 1961–1990, 1971–2000, 1981–2010, 1991–2020 are noted. The greatest differences are observed in the regime characteristics of the first and the last periods. The absolute maximum or minimum recorded in at least one of the months from January to May determines the nature of the ice cover of the entire ice season. The sensitivity of the predictive physical-statistical model to the replacement of climatic norms has been investigated. Estimates of the quality of forecasts of the average monthly ice cover are obtained. Keywords: base period, long-term forecast, physical and statistical model, ice cover, climate characteristics, typification, forecast skill scores

COVID-19 Pandemic Development in Jordan-Short-Term and Long-Term Forecasting.

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves’ occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.

Долгосрочное прогнозирование речного стока по спутниковым данным и сезонной информации

This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different AI methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. To enhance the reliability of the proposed models’ outputs, a sub-basin method using the regionalization approach is proposed. Furthermore, to accelerate the training process and achieve more accurate handling of seasonal changes, a parameter representing seasonal variations is introduced. The models are applied to the mountainous Talezang basin, southwestern Iran, for which there is a 14-year series of monthly in-situ data records and snow cover area (SCA) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the use of the sub-basin approach significantly improves both AI methods’ performances. Moreover, it is deduced that the use of seasonal information and satellite data has a great impact on the model performance and accuracy. Comparing the long-term flow forecasts of both models showed that the ANFIS model is superior to the ANN.

Enhanced Long-term and Snow-based Streamflow Forecasting by Artificial Intelligent Methods Using Satellite Imagery and Seasonal Information

This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different artificial intelligent (AI) methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. To enhance the reliability of the proposed models’ outputs, a sub-basin method using the regionalization approach is proposed. Furthermore, to accelerate the training process and achieve more accurate handling of seasonal changes, a parameter representing seasonal variations is introduced. The models are applied to the mountainous Talezang basin, southwestern Iran, for which there is a 14-year series of monthly in-situ data records and snow cover area data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the use of the sub-basin approach significantly improves both AI methods’ performances. Moreover, it is deduced that the use of seasonal information and satellite data has a great impact on the model performance and accuracy. Comparing the long-term flow forecasts of both models showed that the ANFIS model is superior to the ANN.

DC-STGCN: Dual-Channel Based Graph Convolutional Networks for Network Traffic Forecasting

Network traffic forecasting is essential for efficient network management and planning. Accurate long-term forecasting models are also essential for proactive control of upcoming congestion events. Due to the complex spatial-temporal dependencies between traffic flows, traditional time series forecasting models are often unable to fully extract the spatial-temporal characteristics between the traffic flows. To address this issue, we propose a novel dual-channel based graph convolutional network (DC-STGCN) model. The proposed model consists of two temporal components that characterize the daily and weekly correlation of the network traffic. Each of these two components contains a spatial-temporal characteristics extraction module consisting of a dual-channel graph convolutional network (DCGCN) and a gated recurrent unit (GRU). The DCGCN further consists of an adjacency feature extraction module (AGCN) and a correlation feature extraction module (PGCN) to capture the connectivity between nodes and the proximity correlation, respectively. The GRU further extracts the temporal characteristics of the traffic. The experimental results based on real network data sets show that the prediction accuracy of the DC-STGCN model overperforms the existing baseline and is capable of making long-term predictions.

Information Models for Forecasting Nonlinear Economic Dynamics in the Digital Era

The aim of this study was to develop an adequate mathematical model for long-term forecasting of technological progress and economic growth in the digital age (2020-2050). In addition, the task was to develop a model for forecast calculations of labor productivity in the symbiosis of “man + intelligent machine”, where an intelligent machine (IM) is understood as a computer or robot equipped with elements of artificial intelligence (AI), as well as in the digital economy as a whole. In the course of the study, it was shown that in order to implement its goals the Schumpeter-Kondratiev innovation and cycle theory on forming long waves (LW) of economic development influenced by a powerful cluster of economic technologies engendered by industrial revolutions is most appropriate for a long-term forecasting of technological progress and economic growth. The Solow neoclassical model of economic growth, synchronized with LW, gives the opportunity to forecast economic dynamics of technologically advanced countries with a greater precision up to 30 years, the time which correlates with the continuation of LW. In the information and digital age, the key role among the main factors of growth (capital, labour and technological progress) is played by the latter. The authors have developed an information model which allows for forecasting technological progress basing on growth rates of endogenous technological information in economics. The main regimes of producing technological information, corresponding to the eras of information and digital economies, are given in the article, as well as the Lagrangians that engender them. The model is verified on the example of the 5th information LW for the US economy (1982-2018) and it has had highly accurate approximation for both technological progress and economic growth. A number of new results were obtained using the developed information models for forecasting technological progress. The forecasting trajectory of economic growth of developed countries (on the example of the USA) on the upward stage of the 6th LW (2018-2042), engendered by the digital technologies of the 4th Industrial Revolution is given. It is also demonstrated that the symbiosis of human and intelligent machine (IM) is the driving force in the digital economy, where man plays the leading role organizing effective and efficient mutual work. Authors suggest a mathematical model for calculating labour productivity in the digital economy, where the symbiosis of “human + IM” is widely used. The calculations carried out with the help of the model show: 1) the symbiosis of “human + IM” from the very beginning lets to realize the possibilities of increasing work performance in the economy with the help of digital technologies; 2) the largest labour productivity is achieved in the symbiosis of “human + IM”, where man labour prevails, and the lowest labour productivity is seen where the largest part of the work is performed by IM; 3) developed countries may achieve labour productivity of 3% per year by the mid-2020s, which has all the chances to stay up to the 2040s.

Predicting the exchange rate in Sudan using neural networks models during the period (1960 - 2017): التنبؤ بسعر الصرف في السودان باستخدام نماذج الشبكات العصبية خلال الفترة (1960م – 2017م)

The study examined the use of artificial neural network models to predict the exchange rate in Sudan through annual exchange rate data between the US dollar and the Sudanese pound. This study aimed to formulate the models of artificial neural networks in which the exchange rate can be predicted in the coming period. The importance of the study is that it is necessary to use modern models to predict instead of other classical models. The study hypothesized that the models of artificial neural networks have a high ability to predict the exchange rate. Use models of artificial neural networks. The most important results ability of artificial neural networks models to predict the exchange rate accurately, Form MLP (1-1-1) is the best model chosen for that purpose. The study recommended the development of the proposed model for long-term forecasting.

Tourism Demand Forecasting: A Decomposed Deep Learning Approach

Tourism planners rely on accurate demand forecasting. However, despite numerous advancements, crucial methodological issues remain unaddressed. This study aims to further improve the modeling accuracy and advance the artificial intelligence (AI)-based tourism demand forecasting methods. Deep learning models that predict tourism demand are often highly complex and encounter overfitting, which is mainly caused by two underlying problems: (1) access to limited data volumes and (2) additional explanatory variable requirement. To address these issues, we use a decomposition method that achieves high accuracy in short- and long-term AI-based forecasting models. The proposed method effectively decomposes the data and increases accuracy without additional data requirement. In conclusion, this study alleviates the overfitting issue and provides a methodological contribution by proposing a highly accurate deep learning method for AI-based tourism demand modeling.

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Purpose This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress. Design/methodology/approach A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models. Findings The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models. Originality/value The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

A Long-Term Forecast Method for the Investment Demand of Power Grid Based on Linear Regression and Error Correction of Variable

In the long-term forecast for the investment demand of power grid, the error of independent variable will result in a large error between forecast result and actual result. In this paper, the correction parameters of factors which have an influence on investment demand of power grid is introduced and the correction model of long-term forecast is built. Firstly, through the correlation analysis and the principle comment analysis (PCA), the variables which the model needs are selected. Secondly, the long-term forecast model based on linear regression is built and the variable coefficient is calculated through least square method. Thirdly, after analyzing historical data of forecast value and actual value, correction model of long-term forecast is built through introducing correction parameters of each variables. Finally, compared with the calculation results which get from correction model and the long-term forecast model, it is proved that the error of correction model is smaller.