Regime Change

Denotes a significant alteration in the characteristics or behavior of a data stream, leading to challenges for models previously trained on that data. An example is a trading algorithm optimized for bull markets struggling in bear market conditions due to the fundamental differences in market dynamics. Recognizing a regime change is critical for maintaining model performance, as it necessitates updating the model with new, relevant training data to adapt to the changed environment. This adjustment ensures the model remains effective and accurate in predicting outcomes under the new regime, highlighting the importance of flexibility and adaptability in model design and training.