Self-supervised learning for fast and scalable time series hyper-parameter tuning

• URL: http://arxiv.org/abs/2102.05740v1
• Date: Wed, 10 Feb 2021 21:16:13 GMT
• Title: Self-supervised learning for fast and scalable time series hyper-parameter tuning
• Authors: Peiyi Zhang, Xiaodong Jiang, Ginger M Holt, Nikolay Pavlovich Laptev, Caner Komurlu, Peng Gao, and Yang Yu
• Abstract summary: Hyper-parameters of time series models play an important role in time series analysis. We propose a self-supervised learning framework for HPT (SSL-HPT)
• Score: 14.9124328578934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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