Doornik Jurgen A, Castle Jennifer L, Hendry David F
Nuffield College, Oxford, UK.
Climate Econometrics and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, UK.
Int J Forecast. 2022 Apr-Jun;38(2):453-466. doi: 10.1016/j.ijforecast.2020.09.003. Epub 2020 Sep 12.
We have been publishing real-time forecasts of confirmed cases and deaths from coronavirus disease 2019 (COVID-19) since mid-March 2020 (published at www.doornik.com/COVID-19). These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative regarding short-term developments but without requiring other assumptions about how the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is spreading, or whether preventative policies are effective. Thus, they are complementary to the forecasts obtained from epidemiological models. The forecasts are based on extracting trends from windows of data using machine learning and then computing the forecasts by applying some constraints to the flexible extracted trend. These methods have been applied previously to various other time series data and they performed well. They have also proved effective in the COVID-19 setting where they provided better forecasts than some epidemiological models in the earlier stages of the pandemic.
自2020年3月中旬以来，我们一直在发布2019冠状病毒病（COVID-19）确诊病例和死亡人数的实时预测（发布于www.doornik.com/COVID-19）。这些预测是对过去和当前数据的短期统计推断。它们假设潜在趋势对短期发展具有参考价值，但无需对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）病毒的传播方式或预防政策是否有效做出其他假设。因此，它们与从流行病学模型获得的预测互为补充。这些预测基于使用机器学习从数据窗口中提取趋势，然后通过对灵活提取的趋势施加一些约束来计算预测。这些方法此前已应用于各种其他时间序列数据，并且表现良好。它们在COVID-19环境中也被证明是有效的，在疫情早期阶段，它们提供的预测比一些流行病学模型更好。
