Papers › An Analysis of Linear Time Series Forecasting Models

An Analysis of Linear Time Series Forecasting Models

21 Mar 2024arXiv:2403.14587archive 2025-07-28

William Toner, Luke Darlow

Despite their simplicity, linear models perform well at time series forecasting, even when pitted against deeper and more expensive models. A number of variations to the linear model have been proposed, often including some form of feature normalisation that improves model generalisation. In this paper we analyse the sets of functions expressible using these linear model architectures. In so doing we show that several popular variants of linear models for time series forecasting are equivalent and functionally indistinguishable from standard, unconstrained linear regression. We characterise the model classes for each linear variant. We demonstrate that each model can be reinterpreted as unconstrained linear regression over a suitably augmented feature set, and therefore admit closed-form solutions when using a mean-squared loss function. We provide experimental evidence that the models under inspection learn nearly identical solutions, and finally demonstrate that the simpler closed form solutions are superior forecasters across 72% of test settings.

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Code

sir-lab/linear-forecasting officialmentioned on GitHub report
levi-ackman/lino mentioned on GitHubpytorch report
levi-ackman/refocus mentioned on GitHubpytorch report

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FormTime SeriesTime Series Forecastingregression

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Time Series Forecasting ETTh1 (336) Multivariate OLS MSE 0.448 #38 of 72 Archive leaderboard report

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Methods

Linear Regression

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