mlsanityData to 7 Oct 2026

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

arXiv 2102.05740 ↗First posted 10 Feb 2021cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 5 of its 21 references that are in the map. First posted on arXiv 10 Feb 2021.

Builds on

5 of 21 references.

References · 5

  1. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  2. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 791
  3. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  4. 2019S4L: Self-Supervised Semi-Supervised Learningcited by 335
  5. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 213

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Cited by year

Citing papers per year: 2022: 2; 2023: 1.

3 papers.

  1. 2022Unsupervised Model Selection for Time-series Anomaly Detectioncited by 24
  2. 2022MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detectioncited by 5
  3. 2023Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Servicescited by 0