Information Fusion Technique for Weighted Time Series Model

In this paper, we propose an information fusion technique for weighted time series model, is called OWA-MA forecasting model. The OWA-MA forecasting model combines OWA operator and weighted moving average (WMA). The model deals with the dynamical weighting problem more rationally and flexibly according to the situational parameter alpha value from the user´s viewpoint. For verifying proposed method, we use two datasets to illustrate our performance, the datasets are: dataset 1 - the yearly data on enrollments at the university of Alabama and dataset 2 - the forecast demand table to evaluate the proposed model. Furthermore, the tracking signal as evaluation criteria to compares the proposed model with other models. It is shown that our proposed method proves better than other methods for time series model.