Planning based on long and short term time series forecasts is a common
practice across many industries. In this context, temporal aggregation and
reconciliation techniques have been useful in improving forecasts, reducing
model uncertainty, and providing a coherent forecast across different time
horizons. However, an underlying assumption spanning all these techniques is
the complete availability of data across all levels of the temporal hierarchy,
while this offers mathematical convenience but most of the time low frequency
data is partially completed and it is not available while forecasting. On the
other hand, high frequency data can significantly change in a scenario like the
COVID pandemic and this change can be used to improve forecasts that will
otherwise significantly diverge from long term actuals. We propose a dynamic
reconciliation method whereby we formulate the problem of informing low
frequency forecasts based on high frequency actuals as a Markov Decision
Process (MDP) allowing for the fact that we do not have complete information
about the dynamics of the process. This allows us to have the best long term
estimates based on the most recent data available even if the low frequency
cycles have only been partially completed. The MDP has been solved using a Time
Differenced Reinforcement learning (TDRL) approach with customizable actions
and improves the long terms forecasts dramatically as compared to relying
solely on historical low frequency data. The result also underscores the fact
that while low frequency forecasts can improve the high frequency forecasts as
mentioned in the temporal reconciliation literature (based on the assumption
that low frequency forecasts have lower noise to signal ratio) the high
frequency forecasts can also be used to inform the low frequency forecasts.

本文提出了一种基于马尔可夫决策过程的动态和可定制的时间差分强化学习方法，旨在利用高频实际数据来改进低频数据的预测，进而改善长期预测的准确性。该方法相较于仅使用历史低频数据，显著提升了长期预测效果，并突显出低频预测可以提升高频预测，同时高频数据也可以为低频预测提供影响。