Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

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Backpropagation neural network models for LiFePO4 battery

10.1063/1.4958527 ◽

2016 ◽

Author(s):

Ungu Primadusi ◽

Adha Imam Cahyadi ◽

Dani Prasetyo ◽

Oyas Wahyunggoro

Keyword(s):

Neural Network ◽

Network Models ◽

Backpropagation Neural Network ◽

Neural Network Models ◽

Lifepo4 Battery

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Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University

International Journal on Advanced Science Engineering and Information Technology ◽

10.18517/ijaseit.11.2.11152 ◽

2021 ◽

Vol 11 (2) ◽

pp. 610

Author(s):

Ainul Yaqin ◽

Arif Dwi Laksito ◽

Siti Fatonah

Keyword(s):

Neural Network ◽

Network Models ◽

Early Prediction ◽

Backpropagation Neural Network ◽

Neural Network Models

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Forecasting Cryptocurrency Prices Using ARIMA and Neural Network: A Comparative Study

The Journal of Prediction Markets ◽

10.5750/jpm.v13i2.1780 ◽

2020 ◽

Author(s):

Saurabh Kumar

Keyword(s):

Neural Network ◽

Time Horizon ◽

Moving Average ◽

Arima Model ◽

Network Models ◽

Sample Period ◽

Neural Network Models ◽

Time Horizons ◽

The World ◽

Auto Regressive

The prices of cryptocurrencies are very volatile and forecasting them is a challenging task for the researchers across the world. The present study examines the accuracy of forecasted returns of the two most popular cryptocurrencies (Bitcoin and Ethereum) for the sample period spanning from October 1, 2013, to November 30, 2018. Auto-regressive integrated moving average (ARIMA) and Neural Network models have been used to forecast the returns of the cryptocurrencies. The forecasting results for different time-horizons indicate that for a shorter time-horizon, ARIMA model is better for forecasting the returns of cryptocurrencies, whereas, for a longer time-horizon, Neural Network model is better for forecasting the returns of cryptocurrencies. These results have implications for traders, investors, regulators, policymakers and academia.

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Prediction of Area and Production of Groundnut Using Box-Jenkins Arima and Neural Network Approach

Journal of Reliability and Statistical Studies ◽

10.13052/jrss0974-8024.13244 ◽

2020 ◽

Author(s):

S. T. Pavana Kumar ◽

Ferdinand B. Lyngdoh

Keyword(s):

Neural Network ◽

Moving Average ◽

Arima Model ◽

Network Models ◽

Arima Models ◽

Neural Network Models ◽

Neural Network Approach ◽

Data Set ◽

The Neural Network ◽

Hidden Layer

Selection of parameters for Auto Regressive Integrated Moving Average (ARIMA) model in the prediction process is one of the most important tasks. In the present study, groundnut data was utlised to decide appropriate p, d, q parameters for ARIMA model for the prediction purpose. Firstly, the models were fit to data without splitting into training and validation/testing sets and evaluated for their efficiency in predicting the area and production of groundnut over the years. Meanwhile, models are compared among other fitted ARIMA models with different p, d, q parameters based on decision criteria’s viz., ME, RMSE, MAPE, AIC, BIC and R-Square. The ARIMA model with parameters p-2 d-1-2, q-1-2 are found adequate in predicting the area as well as production of groundnut. The model ARIMA (2, 2, 2) and ARIMA (2,1,1) predicted the area of groundnut crop with minimum error estimates and residual characteristics (ei). The models were fit into split data i.e., training and test data set, but these models’ prediction power (R-Square) declined during testing. In case of predicting the area, ARIMA (2,2,2) was consistent over the split data but it was not consistent while predicting the production over years. Feed-forward neural networks with single hidden layer were fit to complete, training and split data. The neural network models provided better estimates compared to Box-Jenkins ARIMA models. The data was analysed using R-Studio.

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Development and comparison of regression models and feedforward backpropagation neural network models to predict seasonal indoor PM2.5–10 and PM2.5 concentrations in naturally ventilated schools

Atmospheric Pollution Research ◽

10.1016/j.apr.2015.09.001 ◽

2015 ◽

Vol 6 (6) ◽

pp. 1013-1023 ◽

Cited By ~ 24

Author(s):

Maher Elbayoumi ◽

Nor Azam Ramli ◽

Noor Faizah Fitri Md Yusof

Keyword(s):

Neural Network ◽

Regression Models ◽

Network Models ◽

Backpropagation Neural Network ◽

Neural Network Models ◽

Indoor Pm2.5

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Forecasting tourism demand using search query data: A hybrid modelling approach

Tourism Economics ◽

10.1177/1354816618768317 ◽

2019 ◽

Vol 25 (3) ◽

pp. 309-329 ◽

Cited By ~ 9

Author(s):

Long Wen ◽

Chang Liu ◽

Haiyan Song

Keyword(s):

Linear Models ◽

Moving Average ◽

Mainland China ◽

Network Models ◽

Tourism Demand ◽

Search Query ◽

Neural Network Models ◽

Autoregressive Integrated Moving Average ◽

Artificial Neural Network Models ◽

Component Models

Search query data have recently been used to forecast tourism demand. Linear models, particularly autoregressive integrated moving average with exogenous variable models, are often used to assess the predictive power of search query data. However, they are limited by their inability to model non-linearity due to their pre-assumed linear forms. Artificial neural network models could be used to model non-linearity, but mixed results indicate that their application is not appropriate in all situations. Therefore, this study proposes a new hybrid model that combines the linear and non-linear features of component models. The model outperforms other models when forecasting tourist arrivals in Hong Kong from mainland China, thus demonstrating the advantage of adopting hybrid models in forecasting tourism demand with search query data.

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Forecasting ozone concentration levels using Box-Jenkins ARIMA modelling and artificial neural networks: A comparative study

MATEMATIKA ◽

10.11113/matematika.v33.n2.900 ◽

2017 ◽

Vol 33 (2) ◽

pp. 119 ◽

Cited By ~ 1

Author(s):

Norhashidah Awang ◽

Ng Kar Yong ◽

Soo Yin Hoeng

Keyword(s):

Neural Network ◽

Moving Average ◽

Back Propagation ◽

Network Models ◽

Absolute Error ◽

Percentage Error ◽

Daily Maximum ◽

Neural Network Models ◽

Artificial Neural ◽

Concentration Levels

An accurate forecasting of tropospheric ozone (O3) concentration is beneficial for strategic planning of air quality. In this study, various forecasting techniques are used to forecast the daily maximum O3 concentration levels at a monitoring station in the Klang Valley, Malaysia. The Box-Jenkins autoregressive integrated moving-average (ARIMA) approach and three types of neural network models, namely, back-propagation neural network, Elman recurrent neural network and radial basis function neural network are considered. The daily maximum data, spanning from 1 January 2011 to 7 August 2011, was obtained from the Department of Environment, Malaysia. The performance of the four methods in forecasting future values of ozone concentrations is evaluated based on three criteria, which are root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The findings show that the Box-Jenkins approach outperformed the artificial neural network methods.

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