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• W4383500722 abstract "We use “glide charts” (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice. We first use them to evaluate the simple feature-engineered linear regression (FELR) forecasts of Diebold and Göbel (2022), and to compare FELR forecasts to naive pure-trend benchmark forecasts. Then we introduce a much more sophisticated feature-engineered machine learning (FEML) model, and we use glide charts to evaluate FEML forecasts and compare them to a FELR benchmark. Our substantive results include the frequent appearance of predictability thresholds, which differ across months, meaning that accuracy initially fails to improve as the target date is approached but then increases progressively once a threshold lead time is crossed. Also, we find that FEML can improve appreciably over FELR when forecasting “turning point” months in the annual cycle at horizons of one to three months ahead." @default.
• W4383500722 created "2023-07-08" @default.
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• W4383500722 date "2023-08-01" @default.
• W4383500722 modified "2023-10-18" @default.
• W4383500722 title "Assessing and comparing fixed-target forecasts of Arctic sea ice: Glide charts for feature-engineered linear regression and machine learning models" @default.
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• W4383500722 doi "https://doi.org/10.1016/j.eneco.2023.106833" @default.
• W4383500722 hasPublicationYear "2023" @default.
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