A granular time series approach to long-term forecasting and trend forecasting
AbstractTo 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 387 (2008)
Issue (Month): 13 ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Information granules; Granular time series; Forecasting; Long-term forecasting; Time series; Trend forecasting;
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Monash Econometrics and Business Statistics Working Papers
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