Regime Switch Detection with Global LSTM-SSM

In this seminar, I presented a global LSTM-SSM approach for detecting regime switches in data streams using the Switching Kalman Filter.

While the existing method relies on locally trained LSTM models, I showed how to leverage additional time series data to train a single global LSTM, enabling more scalable and generalizable anomaly detection.

Source: CIV-ML’s Seminars