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Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models
International Journal of Banking, Accounting and Finance, 2014The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to predict the stock index returns. The performance of ARIMA-
M Thenmozhi
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TEMPORAL AGGREGATION IN THE ARIMA PROCESS
Journal of Time Series Analysis, 1986Abstract. The effect of temporal aggregation on ARIMA models is investigated. The paper discusses the change of model form resulting from aggregation. For the IMA model it is noted that reduction of model order may occur, due to aggregation, which takes an arbitrarily high order IMA (d, q) process to an IMA (d, 0) process for the aggregates.
Stram, Daniel O., Wei, William W. S.
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2019
Chapter 2 introduces ARIMA algebra. With a few exceptions, this material mirrors the authors’ earlier work. The chapter begins with stationary time series processes – white noise, moving average (MA), and autoregressive (AR) processes – and moves predictably to non-stationary and multiplicative (seasonal) models. Stationarity implies
David McDowall +2 more
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Chapter 2 introduces ARIMA algebra. With a few exceptions, this material mirrors the authors’ earlier work. The chapter begins with stationary time series processes – white noise, moving average (MA), and autoregressive (AR) processes – and moves predictably to non-stationary and multiplicative (seasonal) models. Stationarity implies
David McDowall +2 more
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EPSTO-ARIMA: Electric Power Stochastic Optimization Predicting Based on ARIMA
2021 IEEE 9th International Conference on Smart City and Informatization (iSCI), 2021Yuqing Xu +3 more
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Comparison Between ARIMA and EEMD+ARIMA Models in Forecasting Electricity Consumption
Fusion: Practice and ApplicationsAccurate forecasting of future electricity consumption is necessary to create a satisfactory design for an electricity distribution system. To enhance forecasting accuracy, autoregressive integrated moving average (ARIMA) was compared with hybrid of ensemble empirical mode decomposition (EEMD) plus autoregressive integrated moving average (ARIMA ...
Abdulsalam Elnaeem .., Ani Bin Shabri
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Well production forecasting based on ARIMA-LSTM model considering manual operations
Energy, 2021Dongyan Fan, Hai Sun, Kai Zhang
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An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique
Applied Soft Computing Journal, 2023Soumik Ray +2 more
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Prediction of COVID-19 Data Using an ARIMA-LSTM Hybrid Forecast Model
Mathematics, 2022Yongchao Jin +2 more
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