Feature enhancement for model selection in time series forecasting

Selecting the most appropriate forecasting model for certain time series may utilize the similarity between time series. Previous literature defined several global characteristics of time series as similarity measure. This paper attempts to enhance those characteristics by the coefficients of polynomial function. Considering that not all features may be useful for categorization, we employ feature selection to choose the most discriminating features. In addition, we select a forecasting method based on its previous performance on similar dataset. Hence, there is no need to train the current dataset against all predictors. The pool of predictors ranges from simple to sophisticated ones, namely polynomial interpolation, automatic ARIMA, and Multiple Kernel Learning. The dataset used for experiment is the 3003 records from M3 competition to construct the historical database and 111 records from the M1 competition as testing dataset. Our experimental results indicate that our feature enhancement for model selection may improve the forecasting performance.