  In the context of time series forecasting, it is a common practice to
evaluate multiple methods and choose one of these methods or an ensemble for
producing the best forecasts. However, choosing among different ensembles over
multiple methods remains a challenging task that undergoes a combinatorial
explosion as the number of methods increases. In the context of demand
forecasting or revenue forecasting, this challenge is further exacerbated by a
large number of time series as well as limited historical data points available
due to changing business context. Although deep learning forecasting methods
aim to simultaneously forecast large collections of time series, they become
challenging to apply in such scenarios due to the limited history available and
might not yield desirable results. We propose a framework for forecasting short
high-dimensional time series data by combining low-rank temporal matrix
factorization and optimal model selection on latent time series using
cross-validation. We demonstrate that forecasting the latent factors leads to
significant performance gains as compared to directly applying different
uni-variate models on time series. Performance has been validated on a
truncated version of the M4 monthly dataset which contains time series data
from multiple domains showing the general applicability of the method.
Moreover, it is amenable to incorporating the analyst view of the future owing
to the low number of latent factors which is usually impractical when applying
forecasting methods directly to high dimensional datasets.
