Long-Term Forecasting of Time Series Based on Sliding Window Information Granules and Fuzzy Inference System

In this paper, we propose a new granular time series approach based on sliding window information granules and fuzzy inference system for predicting long-term uncertain data sets. The sliding window information granules, which extract and choose more and useful temporal segments from the initial time series, can capture more essential relationships. And fuzzy inference system based on weighted average is used to predict the future segment window. The experimental results illustrate the validity of the proposed method.

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