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.

翻译：我们使用“滑行图”（即随着目标日期临近，均方根预测误差序列的图）来评估和对比北极海冰的固定目标预测。首先，利用滑行图评估Diebold和Goebel（2021）提出的简单特征工程线性回归（FELR）预测，并将其与朴素纯趋势基准预测进行对比。随后，我们引入一个更为复杂的特征工程机器学习（FEML）模型，并借助滑行图评估FEML预测，同时将其与FELR基准进行比较。实质性研究结果包括：可预测性阈值的频繁出现——该阈值因月份而异，意味着准确性在目标日期临近时最初并未改善，但一旦超过特定提前期的阈值，准确性便逐步提升。此外，我们发现，在预测年度循环中的“转折点”月份（提前一至三个月的预测期）时，FEML相比FELR可取得显著改进。