Time Series Modeling with Fuzzy Cognitive Maps based on Partitioning Strategies

Abstract

The change of amplitude and frequency result in a variety of variation modality of time series in the universe. It is difficult to describe the variation features of time series exactly relying solely on a single simulating model. To overcome this limitation, a new prediction model using fuzzy cognitive maps is proposed based on partitioning strategies. Initially, fuzzy c-mean clustering is adopted to partition time series into several sub-sequences. Consequently, each partition has its corresponding sequences. Subsequently these sub-sequences are used to constructed fuzzy cognitive maps models respectively. Finally, the fuzzy cognitive maps models are merged by fuzzy rules. The constructed model is not only performing well in numerical prediction but also has interpretability. The experimental results show that the model based on partition strategy is superior to the single.