why xgboost algorithm is not useful for anomaly detection on time series? There are some cases about forecasting on time series. (https://www.kaggle.com/code/robikscube/tutorial-time-series-forecasting-with-xgboost) is there an implementation we could use this algorithm for anomaly detection and forecasting together on time series data?
1 Answers
Anomalies by definition are rare. Generally, standard classification algorithms have issues due to the objective function when one of the classes are rare.
If you wanted to detect anomalies, one of the things that you can try is to use xgboost to predict the time series, and then use the residual to determine which are "poorly" predicted by the algorithm and therefore are anomalous.
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