Period-aware local modelling and data selection for time series prediction

The paper tackles with local models (LM) for periodical time series (TS) prediction. A novel prediction method is introduced, which achieves high prediction accuracy by extracting relevant data from historical TS for LMs training. According to the proposed method, the period of TS is determined by using autocorrelation function and moving average filter. A segment of relevant historical data is determined for each time step of the TS period. The data for LMs training are selected on the basis of the k-nearest neighbours approach with a new hybrid usefulness-related distance. The proposed definition of hybrid distance takes into account usefulness of data for making predictions at a given time step. During the training procedure, only the most informative lags are taken into account. The number of most informative lags is determined in accordance with the Kraskov's mutual information criteria. The proposed approach enables effective applications of various machine learning (ML) techniques for prediction making in expert and intelligent systems. Effectiveness of this approach was experimentally verified for three popular ML methods: neural network, support vector machine, and adaptive neuro-fuzzy inference system. The complexity of LMs was reduced by TS preprocessing and informative lags selection. Experiments on synthetic and real-world datasets, covering various application areas, confirm that the proposed period aware method can give better prediction accuracy than state-of-the-art global models and LMs. Moreover, the data selection reduces the size of training dataset. Hence, the LMs can be trained in a shorter time.