Remember, Don’t Descend: Online Retrieval Episodic Memory for Test-Time Adaptation in Time-Series Forecasting

Time-series forecasting models often suffer performance degradation after deployment as the data distribution evolves over time. Existing test-time adaptation (TTA) methods for time-series forecasting address this issue by continually updating lightweight adaptation parameters using newly observed values. However, such updates persist feedback from a particular time point in the model parameters, so an inappropriate update may adversely affect subsequent predictions that are unrelated to the context in which the update was made. This raises a fundamental question: how can a forecasting model effectively adapt to a changing environment by exploiting the large amount of data that accumulates at inference time? We answer this question with a substantially simpler approach based on an online memory bank, demonstrating that it can outperform the prevailing paradigm of parameter-update-based TTA without requiring continual parameter optimization. Our findings suggest that time-series TTA can move beyond adaptation centered on per-step parameter updates toward a memory-centric adaptation paradigm, in which experience accumulated over time is explicitly retained and leveraged to continually respond to evolving distributions. Across five forecasting backbones, six datasets, and four forecast horizons, our method, OREM (Online Retrieval Episodic Memory), reduces MSE and MAE by $5.43\%$ and $3.37\%$ on average, respectively. OREM improves MSE over the frozen predictor in all 120 experimental settings. OREM also substantially reduces harmful corrections to the frozen backbone's forecasts compared with existing TTA methods, resulting in more reliable forecast adaptation.