Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion

Xindong Zhang · Hongyinping Feng

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

Abstract

Abstract Financial time series prediction is regarded as one of the most challenging job because of its inherent complexity, and the hybrid forecasting model incorporating autoregressive integrated moving average and support vector machine (SVM) has been implemented widely to deal with the both linear and nonlinear patterns in time series data. However, the SVM model does not take into consideration the time correlation knowledge between different data points in time series, which impacts the learning efficiency of the SVM in real application. To overcome this restriction, this paper proposes the Taylor Expansion Forecasting model as an alternative to the SVM and develops a novel hybrid methodology via combining autoregressive integrated moving average and Taylor Expansion Forecasting to exploit the comprehensive forecasting capacity to the financial time series data with noise. Both theoretical proof and empirical results obtained on several commodity future prices demonstrate that the proposed hybrid model improves greatly the forecasting accuracy.

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References

2003Time series forecasting using a hybrid ARIMA and neural network model 2003Financial time series forecasting using support vector machines 200625 years of time series forecasting 1992Error measures for generalizing about forecasting methods: Empirical comparisons 2001Application of support vector machines in financial time series forecasting 2004Forecasting stock market movement direction with support vector machine 2004A hybrid ARIMA and support vector machines model in stock price forecasting 2008Surveying stock market forecasting techniques – Part II: Soft computing methods

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