Large-scale experiments on synthetic data find stationarity transformations improve forecasts in only 18% of matched cases, with variance stabilization as the main exception and signal attenuation as the mechanism.
LightGBM: A Highly Efficient Gradient Boosting Decision Tree.Advances in Neural Information Processing Systems2017,30, 3146–3154
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Do Stationarity Transformations Actually Improve Time Series Forecasts? A Controlled Experimental Evaluation
Large-scale experiments on synthetic data find stationarity transformations improve forecasts in only 18% of matched cases, with variance stabilization as the main exception and signal attenuation as the mechanism.