Existing incremental fuzzy clustering algorithms which can be used for time series often require setting multiple control parameters.To solve this problem

a fuzzy clustering algorithm of time series based on adaptive incremental learning is proposed.First

the cluster structure information obtained by the previous clustering process is inherited to initialize the current clustering process.Then

the outliers in current data block are adaptively searched without parameters

and new clusters are automatically created from the outliers.Finally

an empty cluster flag is checked to determine if some clusters need to be removed to ensure the favorable efficiency of subsequent clustering.The experimental results show that the proposed algorithm has good clustering accuracy and efficiency for both equal-length and unequal-length time series.