Employing local modeling in machine learning based methods for time-series prediction

•Propose a local modeling strategy for time series prediction.•Consider the trend of a time series by the use of hybrid distance.•Proper lags are selected by the use of mutual information.•Develop algorithms to extract training patterns from historical data.•Show the effectiveness of local modeling by experiments on real-world datasets.

Time series prediction has been widely used in a variety of applications in science, engineering, finance, etc. There are two different modeling options for constructing forecasting models in time series prediction. Global modeling constructs a model which is independent from user queries. On the contrary, local modeling constructs a local model for each different query from the user. In this paper, we propose a local modeling strategy and investigate the effectiveness of incorporating local modeling with three popular machine learning based forecasting methods, Neural Network (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Least Squares Support Vector Machine (LS-SVM), for time series prediction. Given a series of historical data, a local context of the user query is located and an appropriate number of lags are selected. Then forecasting models are constructed by applying NN, ANFIS, and LS-SVM, respectively. A number of experiments are conducted and the results show that local modeling can enhance the estimation performance of a forecasting method for time series prediction.