Dynamic Temporal Reconciliation by Reinforcement learning. (arXiv:2201.11964v1 [cs.LG])

Web: http://arxiv.org/abs/2201.11964

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 …

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