Multiple Cycles of Time Series Anomaly Detection Algorithm Based on Wavelet Analysis

Abstract

In view of the hydrological time series data with both trends, jumping, and the cycle characteristics of the certainty together with randomness of the unique features, this paper comes up with wavelet analysis to analyze the main cycle and hidden cycle, then through the sliding window method to predict data based on each period for further testing. And verify this method with instance data. The experimental results show that multiple cycles of time series anomaly detection algorithm based on wavelet analysis can effectively complete the anomaly detection of hydrological time series data.