Abstract
To overcome the "curse of dimensionality" (which plagues most predictors (predictive models) when carrying out long-term forecasts) and cope with uncertainty present in many time series, in this study, we introduce a concept of granular time series which are used to long-term forecasting and trend forecasting. A technique of fuzzy clustering is used to construct information granules on a basis of available numeric data present in the original time series. In the sequel, we develop a forecasting model which captures the essential relationships between such information granules and in this manner constructs a fundamental forecasting mechanism. It is demonstrated that the proposed model comes with a number of advantages which manifest when processing a large number of data. Experimental evidence is provided through a series of examples using which we quantify the performance of the forecasting model and provide with some comparative analysis. (c) 2008 Elsevier B.V. All rights reserved.