【报告摘要】Forecast reconciliation ensures forecasts of time series in a hierarchy adhere to aggregation constraints, enabling aligned decision making. While forecast reconciliation can enhance overall accuracy in a hierarchical or grouped structure, it can lead to worse forecasts for certain series, with the greatest gains typically seen in series that originally have poorly performing base forecasts. In practical applications, some series in a structure often produce poor base forecasts due to model misspecification or low forecastability. To mitigate their negative impact, we propose two categories of forecast reconciliation methods that incorporate automatic time series selection based on out-of-sample and in-sample information, respectively. These methods keep “poor” base forecasts unused in forming reconciled forecasts, while adjusting the weights assigned to the remaining series accordingly when generating bottom-level reconciled forecasts. Additionally, our methods ameliorate disparities stemming from varied estimators of the base forecast error covariance matrix, alleviating challenges associated with estimator selection.

【报告人简介】王晓倩博士，中国科学院数学与系统科学研究院预测科学研究中心助理研究员。主要研究方向包括时间序列预测、大规模数据分析、分布式计算等。作为项目负责人主持国际预测者协会及 SAS 联合资助的预测方法研究项目（IIF-SAS Grant to Promote Research on Forecasting）。目前已在 European Journal of Operational Research、International Journal of Forecasting、Journal of the Operational Research Society 等国际权威期刊上发表多篇论文，Google 学术他引 1600 余次。开发并参与开发预测相关的开源 R 包 conformalForecast 和 forecast，均已发布于 CRAN。目前担任 R Journal 副主编、澳大利亚研究理事会 OPTIMA 中心副研究员。