Papers › Time Series Prediction under Distribution Shift using Differentiable Forgetting

Time Series Prediction under Distribution Shift using Differentiable Forgetting

23 Jul 2022arXiv:2207.11486archive 2025-07-28

Stefanos Bennett, Jase Clarkson

Time series prediction is often complicated by distribution shift which demands adaptive models to accommodate time-varying distributions. We frame time series prediction under distribution shift as a weighted empirical risk minimisation problem. The weighting of previous observations in the empirical risk is determined by a forgetting mechanism which controls the trade-off between the relevancy and effective sample size that is used for the estimation of the predictive model. In contrast to previous work, we propose a gradient-based learning method for the parameters of the forgetting mechanism. This speeds up optimisation and therefore allows more expressive forgetting mechanisms.

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