Iterative ARIMA-multiple support vector regression models for long term time series prediction
نویسندگان
چکیده
Support Vector Regression (SVR) has been widely applied in time series forecasting. Considering long term predictions, iterative predictions perform many one-step-ahead predictions until the desired horizon is achieved. This process accumulates the error from previous predictions and may affect the quality of forecasts. In order to improve long term iterative predictions a hybrid multiple Autoregressive Integrated Moving Average(ARIMA)-SVR model is applied to perform predictions considering linear and non-linear components from the time series. The results show that the proposed method produces more accurate predictions in the long term context.
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