Assessing and comparing fixed-target forecasts of Arctic sea ice: Glide charts for feature-engineered linear regression and machine learning models

Francis Diebold, Maximilian Göbel and Philippe Goulet Coulombe

Energy Economics, 2023, vol. 124, issue C

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.

Keywords: Seasonal climate forecasting; Forecast evaluation and comparison; Prediction (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 Q54 (search for similar items in EconPapers)
Date: 2023
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Related works:
Working Paper: Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models (2023)
Working Paper: Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models (2022)
Working Paper: Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:124:y:2023:i:c:s0140988323003316

DOI: 10.1016/j.eneco.2023.106833

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