Input selection in support vector regression for univariate time series forecasting

The main purpose of modeling time series data is for forecasting. Time series modeling can be categorized into the univariate and multivariate approach. In univariate time series modeling, there are several methods of forecasting; one of them is ARIMA. Time series modeling can also consider the exogenous variables to improve the accuracy of forecasting or to obtain a more meaningful model. Recently, machine learning methods are widely employed in various field, including time series forecasting, particularly for solving nonlinear relationship among the variables. One of the most popular machine learning methods is Support Vector Machine (SVM). SVM can solve classification and regression problem. The SVM method used in time series is called Support Vector Regression (SVR). In SVR method, one of the most important things to improve the accuracy of forecasting is input selection. One of the approaches to select input in SVR is by choosing the significant lag obtained from the classical time series analysis and expand the ARIMA model to get multiplicative lag. In addition, the data on daily sales contains seasonal and calendar variation. The models are evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The best method is the SVR method using multiplicative lag from the best ARIMA model without dummy variable.