论文标题
评估和比较北极海冰的固定目标预测：特征设计线性回归和机器学习模型的滑行图
Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models
论文作者
论文摘要
我们使用“滑行图”（在接近目标日期时，均方根平方序列的序列图）来评估和比较北极海冰的固定目标预测。我们首先使用它们来评估Diebold和Goebel（2021）的简单特征工程线性回归（FELR）预测，并将FELR预测与天真的纯趋势基准预测进行比较。然后，我们介绍了更复杂的功能工程机器学习（FEML）模型，并使用滑行图来评估FEM的预测并将其与FELR基准进行比较。我们的实质性结果包括可预测性阈值的频繁出现，这些阈值在几个月中有所不同，这意味着，随着目标日期的临近，准确性最初无法提高，但一旦跨越阈值提前时间，则逐渐增加。此外，我们发现，当预测年度周期中的“转折点”月份的“转折点”月份，FEML可以明显地改善FELR。
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 Goebel (2021), 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.
