Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models

Li, Shuyu; Yang, Xuan; Li, Rongrong

doi:10.3390/su11030695

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…In 2019, Li S et al established a combined model of ARIMA and BPNN (ARIMA-BPNN) using coal consumption data in India. The research showed that the combined model had significantly higher prediction accuracy than the single model [30]. Currently, a combined model based on ARIMA and ERNN (ARIMA-ERNN) is mainly applied to air pollution prediction [31], spot price forecasting [32], error compensation [33] and other fields.…”

Section: Introductionmentioning

confidence: 99%

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis

Zhai

Tie³

et al. 2021

BMC Infect Dis

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Background Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of great significance in controlling brucellosis and taking preventive measures. Methods Our human brucellosis incidence data were extracted from Shanxi Provincial Center for Disease Control and Prevention. We used seasonal-trend decomposition using Loess (STL) and monthplot to analyse the seasonal characteristics of human brucellosis in Shanxi Province from 2007 to 2017. The autoregressive integrated moving average (ARIMA) model, a combined model of ARIMA and the back propagation neural network (ARIMA-BPNN), and a combined model of ARIMA and the Elman recurrent neural network (ARIMA-ERNN) were established separately to make predictions and identify the best model. Additionally, the mean squared error (MAE), mean absolute error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the performance of the model. Results We observed that the time series of human brucellosis in Shanxi Province increased from 2007 to 2014 but decreased from 2015 to 2017. It had obvious seasonal characteristics, with the peak lasting from March to July every year. The best fitting and prediction effect was the ARIMA-ERNN model. Compared with those of the ARIMA model, the MAE, MSE and MAPE of the ARIMA-ERNN model decreased by 18.65, 31.48 and 64.35%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 60.19, 75.30 and 64.35%, respectively. Second, compared with those of ARIMA-BPNN, the MAE, MSE and MAPE of ARIMA-ERNN decreased by 9.60, 15.73 and 11.58%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 31.63, 45.79 and 29.59%, respectively. Conclusions The time series of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models. This will provide some theoretical support for the prediction of infectious diseases and will be beneficial to public health decision making.

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Section: Introductionmentioning

confidence: 99%

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis

Zhai

Tie³

et al. 2021

BMC Infect Dis

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“…Caso contrário, realizam-se a segunda, terceira ou mais diferenças para buscar a estacionariedade. Em geral, as séries não precisam mais do que três defasagens para se tornarem estacionárias (Morretin & Toloi, 1987;Kwiatkowski et al, 1992;Makridakis et al, 1998;Li et al, 2019).…”

Section: Projeções Das Variáveis De Decisão Na Produção De Leite: Objetivo "B"unclassified

Previsões para a produção de leite sob instabilidade pluviométrica no Ceará no período de 1974 a 2019

Paiva

Lemos

Campos

2022

Rev. Econ. Sociol. Rural

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Resumo O setor da pecuária é fortemente influenciado pelos fenômenos climáticos, e a instabilidade pluviométrica se torna um entrave à capacidade produtiva, sobretudo dos municípios pertencentes ao semiárido. Os produtores planejam e organizam sua produção a partir de experiências passadas e criam expectativas quanto ao futuro. Este trabalho avaliou a forma como as variáveis exógenas (pluviometria e preços) interferiam nas previsões dos produtores de leite no Ceará, no período de 1974 a 2019. Geraram-se projeções acerca das variáveis endógenas sobre as quais os produtores de leite tinham poder de decisão (rebanho e produtividade). Embora eles não tivessem poder de decisão sobre o preço médio, que é determinado exogenamente pelo mercado, o estudo fez estimativas das formas de projeções também para essa variável. Estimou-se a maneira como provavelmente a pluviometria pudesse afetar as previsões das variáveis associadas à produção de leite. Utilizou-se do método ARIMA, proposto por Box e Jenkins (1976), para captar o comportamento das variáveis com base em séries históricas (1974-2019). Os resultados confirmaram o impacto indireto das chuvas e dos preços sobre as variáveis endógenas de decisão. As trajetórias das expectativas de produção e dos valores projetados, bem como os testes estatísticos realizados, sinalizaram a robustez dos ajustamentos feitos na pesquisa.

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“…For example, the ARIMA is combined with the neural network model. Li [ 9 ] and others combined MGM and BP neural network with ARIMA to construct MGM-ARIMA and BP-ARIMA to predict coal consumption; Based on combinational prediction, Liu et al [ 10 ] used ARIMA, ANNS and EMS to predict the time series data of PM2.5 concentration; Zhu [ 11 ] proposed a new technique for PM2.5 concentration prediction based on ARIMA and improved BP neural network; Wang et al [ 12 ] proposed an HDIPSO prediction algorithm based on neural networks, which adopted a new speed updating strategy and mutation operation to improve convergence and increase group diversity.…”

Section: Related Workmentioning

confidence: 99%

Prediction of SO2 Concentration Based on AR-LSTM Neural Network

Liu

Fang-ai

2022

Neural Process Lett

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Sulphur dioxide is one of the most common air pollutants, forming acid rain and other harmful substances in the atmosphere, which can further damage our ecosystem and cause respiratory diseases in humans. Therefore, it is essential to monitor the concentration of sulphur dioxide produced in industrial processes in real-time to predict the concentration of sulphur dioxide emissions in the next few hours or days and to control them in advance. To address this problem, we propose an AR-LSTM analytical forecasting model based on ARIMA and LSTM. Based on the sensor’s time series data set, we preprocess the data set and then carry out the modeling and analysis work. We analyze and predict the proposed analysis and prediction model in two data sets and conduct comparative experiments with other comparison models based on the three evaluation indicators of R 2 , RMSE and MAE. The results demonstrated the effectiveness of the AR-LSTM analytical prediction model; Finally, a forecasting exercise was carried out for emissions in the coming weeks using our proposed AR-LSTM analytical forecasting model.

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Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models

Cited by 14 publications

References 41 publications

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis

Previsões para a produção de leite sob instabilidade pluviométrica no Ceará no período de 1974 a 2019

Prediction of SO2 Concentration Based on AR-LSTM Neural Network

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