  Hyper-parameters of time series models play an important role in time series
analysis. Slight differences in hyper-parameters might lead to very different
forecast results for a given model, and therefore, selecting good
hyper-parameter values is indispensable. Most of the existing generic
hyper-parameter tuning methods, such as Grid Search, Random Search, Bayesian
Optimal Search, are based on one key component - search, and thus they are
computationally expensive and cannot be applied to fast and scalable
time-series hyper-parameter tuning (HPT). We propose a self-supervised learning
framework for HPT (SSL-HPT), which uses time series features as inputs and
produces optimal hyper-parameters. SSL-HPT algorithm is 6-20x faster at getting
hyper-parameters compared to other search based algorithms while producing
comparable accurate forecasting results in various applications.
