  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.
