ArticleScientific reports2026
Joint wavelet decomposition of predictors and target variables for drought forecasting.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
We propose a leakage-free wavelet-based methodology for drought prediction that enables both competitive forecasting performance and improved interpretability of predictor-target relationships. Unlike standard time-series decomposition approaches, the wavelet transform is applied within moving windows of the past values, preventing the use of future information during model training. The method merges local and large-scale climate predictors across multiple temporal scales and is evaluated using 76 years of drought data from Illinois, USA. Predictive performance is assessed using RMSE, SMAPE, MASE, R
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