Long-term intuitionistic fuzzy time series forecasting model based on DTW - Xiao-shi FAN; Ying-jie LEI; Yan-li LU; Ya-nan WANG (Tongxin xuebao)
In existing fuzzy time series forecasting models,the intuitionistic fuzzy relationship groups and deterministic transition rules excessively relied on scale of the training data.A long-term intuitionistic fuzzy time series (IFTS) fore-casting model based on DTW was proposed.The IFTS segment base was constructed by IFCM.The complexity of sys-tem was reduced by dynamic update and maintaining of the rule base.The computing method of IFTS segments similar-ity based on the distance of DTW was proposed,which was valid for matching unequal length time series segments.The proposed model implements on the synthetic and the temperature dataset,which including different time series patterns,respectively.The experiments illustrate that the forecasting accuracy of the proposed model is higher than the others on the different tendency patterns of time series.The proposed model overcomes the limitation of single time series pattern and improves the generalization ability.