Do Stationarity Transformations Actually Improve Time Series Forecasts? A Controlled Experimental Evaluation
平稳化变换真的改善时间序列预测吗？受控实验评估AI 解读 ↓
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平稳化变换真的改善时间序列预测吗？受控实验评估
- 研究问题
- 差分等平稳化预处理是否改善预测，效果如何随生成过程变化，选择变换应依靠单位根预检验还是样本外验证？
- 方法与思路
- 作者交叉比较十八类合成过程、十种变换、五类模型和三个期限，将预测还原到原尺度评价，并进行真实序列验证。
- 材料陈述的贡献
- 摘要称匹配结构的去趋势和季节差分可改善准确率，机械差分会降低表现；样本外验证选变换的后悔值低于预检验或固定规则。
- 阅读关注点
- 应核对生成过程与变换的匹配、差分逆变换的锚定方式、验证集划分，以及中介分析和真实序列验证支持多大范围的结论。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；结论来自所考察过程与模型，摘要未报告全部比较细节。
原文摘要
Stationarity transformations, such as differencing, are a common preprocessing step in forecasting, motivated by the idea that modifying a series to achieve stationarity improves accuracy. Whether this is true, and for which processes, has rarely been evaluated in controlled experiments. We study the decision to transform as the object of inquiry. We cross eighteen synthetic data-generating processes, most of them stochastic-trend processes spanning exact and near unit roots, fractional integration, seasonal unit roots, structural breaks, and heteroscedasticity, with ten transformations, five models, and three horizons, replicated by Monte Carlo, for 35,099 evaluations. Each forecast is inverted to the original scale, with the differencing inverse anchored at the forecast origin, and scored by the mean absolute scaled error. Signal-preserving transforms, namely deterministic detrending and seasonal differencing matched to series structure, improve accuracy, whereas indiscriminate differencing degrades it. A mediation analysis shows that differencing achieves trend stationarity, but trend stationarity is only weakly associated with accuracy, and transforms differ in their effects on predictable structure. Choosing the transformation by out-of-sample validation yields lower regret than unit-root pretesting or any fixed rule, with blanket differencing performing the worst. The findings are confirmed by real-world validation on nine series from two domains.