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

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.