Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition

Ina Khandelwal · Ratnadip Adhikari · Ghanshyam Verma

287 citations0/0 files storedProcedia Computer Science

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Forecasting Techniques and Applications Stock Market Forecasting Methods Time Series Analysis and Forecasting

Abstract

Recently Discrete Wavelet Transform (DWT) has led to a tremendous surge in many domains of science and engineering. In this study, we present the advantage of DWT to improve time series forecasting precision. This article suggests a novel technique of forecasting by segregating a time series dataset into linear and nonlinear components through DWT. At first, DWT is used to decompose the in-sample training dataset of the time series into linear (detailed) and non-linear (approximate) parts. Then, the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models are used to separately recognize and predict the reconstructed detailed and approximate components, respectively. In this manner, the proposed approach tactically utilizes the unique strengths of DWT, ARIMA, and ANN to improve the forecasting accuracy. Our hybrid method is tested on four real-world time series and its forecasting results are compared with those of ARIMA, ANN, and Zhang's hybrid models. Results clearly show that the proposed method achieves best forecasting accuracies for each series.

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References

1977Time Series Analysis: Forecasting and Control 2003Time series forecasting using a hybrid ARIMA and neural network model 1998Forecasting with artificial neural networks:1971Time Series Analysis, Forecasting and Control.2003Neural network forecasting for seasonal and trend time series 2010Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm

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