  Selecting the right set of hyperparameters is crucial in time series
forecasting. The classical temporal cross-validation framework for
hyperparameter optimization (HPO) often leads to poor test performance because
of a possible mismatch between validation and test periods. To address this
test-validation mismatch, we propose a novel technique, H-Pro to drive HPO via
test proxies by exploiting data hierarchies often associated with time series
datasets. Since higher-level aggregated time series often show less
irregularity and better predictability as compared to the lowest-level time
series which can be sparse and intermittent, we optimize the hyperparameters of
the lowest-level base-forecaster by leveraging the proxy forecasts for the test
period generated from the forecasters at higher levels. H-Pro can be applied on
any off-the-shelf machine learning model to perform HPO. We validate the
efficacy of our technique with extensive empirical evaluation on five publicly
available hierarchical forecasting datasets. Our approach outperforms existing
state-of-the-art methods in Tourism, Wiki, and Traffic datasets, and achieves
competitive result in Tourism-L dataset, without any model-specific
enhancements. Moreover, our method outperforms the winning method of the M5
forecast accuracy competition.
