Do Stationarity Transformations Actually Improve Time Series Forecasts? A Controlled Experimental Evaluation
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arXiv
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| Natura: | Preprint |
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2026
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| _version_ | 1866916021262090240 |
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| author | Malla, Bhanu Suraj Hu, Yuqing |
| author_facet | Malla, Bhanu Suraj Hu, Yuqing |
| contents | Stationarity transformations are standard preprocessing in time series forecasting, yet their actual impact on accuracy across different non-stationarity types and model families has received little controlled evaluation. We construct synthetic datasets with known properties - trend, seasonality, heteroscedasticity, and combinations - and apply fourteen transformation configurations across seven models and three forecast horizons (3,528 experiments). Stationarity is quantified via consensus ratios from ten statistical tests, and each transform-dataset pair is classified as matched or mismatched based on whether the transform targets the dataset's known non-stationarity. For matched pairs, transforms improve forecasts only 18% of the time. The primary exception is variance stabilization: log and Box-Cox on heteroscedastic data improve accuracy in 60-65% of cases. Differencing a linear-trend series - a textbook use case - worsens forecasts in all cases tested. Mediation analysis confirms that while transforms achieve trend stationarity, this does not translate into lower forecast error; the mechanism is signal attenuation. Real-world validation on TSA airport passenger data corroborates these findings. Our results suggest transformation selection should be guided by empirical out-of-sample evaluation rather than theoretical stationarity assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17689 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Do Stationarity Transformations Actually Improve Time Series Forecasts? A Controlled Experimental Evaluation Malla, Bhanu Suraj Hu, Yuqing Methodology Statistics Theory 62M10, 62P30 G.3; D.2.13; I.6.4 Stationarity transformations are standard preprocessing in time series forecasting, yet their actual impact on accuracy across different non-stationarity types and model families has received little controlled evaluation. We construct synthetic datasets with known properties - trend, seasonality, heteroscedasticity, and combinations - and apply fourteen transformation configurations across seven models and three forecast horizons (3,528 experiments). Stationarity is quantified via consensus ratios from ten statistical tests, and each transform-dataset pair is classified as matched or mismatched based on whether the transform targets the dataset's known non-stationarity. For matched pairs, transforms improve forecasts only 18% of the time. The primary exception is variance stabilization: log and Box-Cox on heteroscedastic data improve accuracy in 60-65% of cases. Differencing a linear-trend series - a textbook use case - worsens forecasts in all cases tested. Mediation analysis confirms that while transforms achieve trend stationarity, this does not translate into lower forecast error; the mechanism is signal attenuation. Real-world validation on TSA airport passenger data corroborates these findings. Our results suggest transformation selection should be guided by empirical out-of-sample evaluation rather than theoretical stationarity assumptions. |
| title | Do Stationarity Transformations Actually Improve Time Series Forecasts? A Controlled Experimental Evaluation |
| topic | Methodology Statistics Theory 62M10, 62P30 G.3; D.2.13; I.6.4 |
| url | https://arxiv.org/abs/2605.17689 |