Abstract
Time series refer to data series classified in chronological order, changing with time and interrelated, and are widely used in different fields. Anomaly detection has always been an important subject in different research fields and applications. This paper aims to study a time series data mining system based on anomaly detection. This paper proposes a time series anomaly detection method to segment, extract and inspect the time series, and uses some important methods of data mining methods. Finally, a new method for time series data extraction-based on neural networks and regression analysis is proposed. Time series data extraction combined model. The experimental results of this paper show that the fuzzy concept is used to solve the limit value situation in order to further improve the accuracy. Finally, the effectiveness of the algorithm is verified through experiments and experimental data, and the practicability of the system is verified through the analysis of time series data extraction.
Published Version
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