AutoAI-TS: AutoAI for Time Series Forecasting
Abstract
A large number of time series forecasting models including traditional statistical models, machine learning models and more recently deep learning have been proposed in the literature. However, choosing the right model along with good parameter values that performs well on a given data is still challenging. Automatically providing a good set of models to users for a given dataset saves both time and effort from using trial-and-error approaches with a wide variety of available models along with parameter optimization. We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset. With its flexible zero-conf design, AutoAI-TS automatically performs all the data preparation, model creation, parameter optimization, training and model selection for users and provides a trained model that is ready to use. For given data, AutoAI-TS utilizes a wide variety of models including classical statistical models, Machine Learning (ML) models, statistical-ML hybrid models and deep learning models along with various transformations to create forecasting pipelines. It then evaluates and ranks pipelines using the proposed T-Daub mechanism to choose the best pipeline. The paper describe in detail all the technical aspects of AutoAI-TS along with extensive benchmarking on a variety of real world data sets for various use-cases. Benchmark results show that AutoAI-TS, with no manual configuration from the user, automatically trains and selects pipelines that on average outperform existing state-of-the-art time series forecasting toolkits.
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“These AutoAI models are sandbox type systems which can be modified and suited to individual requirements [36], [37], [38].”
Method
- Authors
- Shah, S. Y., Patel, D., Vu, L., Dang, X.-H., Chen, B., Kirchner, P., Samulowitz, H., Wood, D., Bramble, G., Gifford, W. M., Ganapavarapu, G., Vaculin, R., & Zerfos, P.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- IBM (Ireland) (IE)
- IBM Thomas J. Watson Research Center, Yorktown Heights , NY, USA
- ORCID
- Venue
- Proceedings of the 2021 International Conference on Management of Data
- Publisher
- ACM
- Published
- 2021-06-09
- Pages
- 2584-2596
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W3170822423
- Cited by
- 39
- References
- 13
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 13 · 2023: 11 · 2022: 6
- FWCI
- 4.57
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGMOD International Conference on Management of Data (SIGMOD) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Keywords
- time series forecasting, automated machine learning, model selection, parameter optimization, deep learning prediction
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