CMBCF: A Cloud Model Based Hybrid Method for Combining Forecast

Forecast combination has been proved to be an effective way to improve the forecasting accuracy. Most of the combining forecast methods now available belong to performance based weighting strategies, which judge the individual models mainly on the basis of their in-sample forecasting accuracy. Less attention has been paid to consider the characteristics underlying the distribution or the shape of forecasts from individual forecasters. However, information hidden in the distributions is of great value because the difference of shapes indicating distinct response towards the same pattern of a certain time series. In this paper, a cloud model based hybrid method for combining forecast(CMBCF) is proposed. In general, the new framework attempts to extract the local distribution characteristics of forecasting series by transforming the series into several cloud models. After the similarity comparison of the series represented in the form of cloud models, CMBCF assigns dynamic weights to individual models and construct the final combining forecast. The experimental results based on widely used time series data sets demonstrate the advantage of CMBCF over several traditional and state-of-art combining forecast strategies.