RobustProphet: Time Series Anomaly Detection Method

Many real-world time series data exhibit outliers and noise like abnormal machine operation, emergency monitoring etc. Anomaly detection is the most important way to deal with it. During all the solutions, Prophet is an effective model for large-scale time series with complicated patterns in anomaly detection and forecasting scenarios. However, Prophet model is easy to fall into overfitting at special time points, which leads to large errors in some behaviors. In this paper we propose a robust complex univariate time series anomaly detection framework RobustProphet based on forecasting. Specifically, in the step of forecasting, linear regression function is adopted to fuse Prophet and XGBoost model which use L2 regularization to resolve the overfitting problem and optimize the prediction results. Once the final forecasting results are generated, we offer a nonparametric dynamic thresholding approach to evaluate residuals to detect anomalies in original time series. We made a comparative study between our proposed model and other classical anomaly detection methods such as Local Outlier Factor (LOF), Isolation Forest on datasets from Yahoo benchmark datasets. The experimental results show that our proposed anomaly detection model has better precision and higher AUC.