Comparative study between Gaussian process regression and long short-term memory neural networks for intraday grid load forecasting

Dkhili, Nouha; Thil, Stéphane; Eynard, Julien; Grieu, Stéphane

doi:10.1109/eeeic/icpseurope49358.2020.9160537

Cited by 1 publication

(1 citation statement)

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“…The proposed sparse GPR model showed that its forecasting capability is superior to ARMA and support vector machine. In [9], the GPR and ANN are studied for intraday distribution grid load forecasting. The results showed that the GPR provided good forecasting values.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian Process Regression Model for Forecasting Stock Exchange of Thailand

2022

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A stock price index measures the change in several share prices, which can describe the market and assist investors in deciding on a specific investment. Thus, foreseeing the stock price index benefits investors in creating a better investment strategy. However, forecasting the stock price index can be challenging due to its non-linearity, non-stationary and high uncertainty. Gaussian process regression (GPR) is an attractive and powerful approach for prediction, especially when the data fluctuates over time with fewer restrictions. Besides, the GPR gains advantages over other forecasting techniques as it can offer predictions with uncertainty to provide margin errors. In this study, we evaluate the use of GPR to predict the stock price of Thailand (SET). The SET data are divided into 2 datasets; the data in the year 2015 - 2020 and the data in the year 2020 due to the massive change during the COVID-19 pandemic. The prediction results from the GPR are then compared to the machine learning approaches, artificial neural network (ANN) and recurrent neural network (RNN) using evaluation scores; the root mean square error (RMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the Nash-Sutcliffe efficiency (NSE). The results indicate that the GPR is superior to the ANN and RNN for both datasets as it provides a high prediction accuracy. Moreover, the results suggest that the GPR is less sensitive to the number of input lags in the model. Therefore, the GPR is more favorable for the prediction of SET than the ANN and RNN. HIGHLIGHTS The Gaussian process regression (GPR) was applied to predict the stock price index of Thailand (SET) The predictive performance of the GPR was compared to artificial neural networks (ANNs) and recurrent neural networks (RNNs) The results indicate that GPR outperformed the other methods as it provided a high prediction accuracy along with prediction intervals GRAPHICAL ABSTRACT

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Section: Introductionmentioning

confidence: 99%