论文标题
超越技能得分：通过法国月份的案例研究探索亚季节预测值
Beyond skill scores: exploring sub-seasonal forecast value through a case study of French month-ahead energy prediction
论文作者
论文摘要
我们量化了对现实世界预测问题的亚季节预测的价值：法国月份的预测。使用表面温度作为预测因子，我们根据实际的能源需求和价格数据来构建交易策略，并评估使用气象预测的财务价值。我们表明，提前时间超过2周的预测对此应用程序具有价值，无论是自己还是与较短的范围预测，尤其是在北方冬季期间。我们考虑了基于此示例的成本/损失框架，并表明，尽管它捕获了短范围预测的性能，但它却错过了较长范围预测的边际价值。我们还将对预测价值的评估与传统技能分数给出的评估进行对比，如果孤立使用，我们表明可能会产生误导。我们强调了对预测技能评估对最终用户实际使用的变量的重要性。
We quantify the value of sub-seasonal forecasts for a real-world prediction problem: the forecasting of French month-ahead energy demand. Using surface temperature as a predictor, we construct a trading strategy and assess the financial value of using meteorological forecasts, based on actual energy demand and price data. We show that forecasts with lead times greater than 2 weeks can have value for this application, both on their own and in conjunction with shorter range forecasts, especially during boreal winter. We consider a cost/loss framework based on this example, and show that while it captures the performance of the short range forecasts well, it misses the marginal value present in the longer range forecasts. We also contrast our assessment of forecast value to that given by traditional skill scores, which we show could be misleading if used in isolation. We emphasise the importance of basing assessment of forecast skill on variables actually used by end-users.
