Time series long-term forecasting model based on information granules and fuzzy clustering
Time series is translated into semantically sound information granules. A modified fuzzy c-means based on dynamic time warping is proposed. The multiple fuzzy rules interpolation is applied to determine predicting variation. Chaotic MackeyGlass, power demand, and daily temperatures time series are chosen. The results show that the proposed model is both accurate and interpretable.
long-term forecasting forecasting information granules dynamic time warping granular time series