A vector forecasting model for fuzzy time series

The emergence of fuzzy time series has recently received more attention because of its capability of dealing with vagueness and incompleteness inherent in data. Deriving an effective and useful forecasting model has been a challenge task. In the previous work, the authors addressed two crucial issues, namely controlling uncertainty and effectively partitioning intervals, as well as developed a deterministic forecasting model to manage these issues. However, their model neglected the distribution and uncertainty of data points and can only provide scalar forecasting, thus limiting its usefulness. This study expands the deterministic forecasting model to improve forecasting capability. We propose a vector forecasting model that allows the prediction of a vector of future values in one step, by integrating the technologies of sliding window and fuzzy c-means clustering, to deal with vector forecasting and interval partitioning. Experimental results and analysis using Monte Carlo simulations for two experiments, both with three data sets, validate the effectiveness of the proposed forecasting model.