首页|Report Summarizes Machine Learning Study Findings from Univer- sity of Quebec Montreal (Assessing and Comparing Fixed-target Forecasts of Arctic Sea Ice: Glide Charts for Feature-engineered Linear Regression and Machine Learning Models)
Report Summarizes Machine Learning Study Findings from Univer- sity of Quebec Montreal (Assessing and Comparing Fixed-target Forecasts of Arctic Sea Ice: Glide Charts for Feature-engineered Linear Regression and Machine Learning Models)
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NETL
NSTL
Elsevier
A new study on Machine Learning is now available. According to news originating from Montreal, Canada, by NewsRx correspondents, research stated, "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 Gobel (2022), and to compare FELR forecasts to naive pure- trend benchmark forecasts." Our news journalists obtained a quote from the research from the University of Quebec Montreal, "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."
MontrealCanadaNorth and Central AmericaCyborgsEmerg- ing TechnologiesEngineeringMachine LearningUniversity of Quebec Montreal
NETL
NSTL
Elsevier
