# AutoAI-TS: AutoAI for Time Series Forecasting

arXiv: https://arxiv.org/abs/2102.12347 · HTML: https://mlsanity.com/paper/2102.12347/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Automated Machine Learning: State-of-The-Art and Open Challenges](https://mlsanity.com/paper/1906.02287/) (2019)
- [Selecting Near-Optimal Learners via Incremental Data Allocation](https://mlsanity.com/paper/1601.00024/) (2015)

## Built on by (most cited first, 15 of 15)

- [TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis](https://mlsanity.com/paper/2210.09693/) (2022)
- [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2308.05566/) (2023)
- [Universal Time-Series Representation Learning: A Survey](https://mlsanity.com/paper/2401.03717/) (2024)
- [Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting](https://mlsanity.com/paper/2211.16126/) (2022)
- [Efficient Automated Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2205.05511/) (2022)
- [Multi-Objective Model Selection for Time Series Forecasting](https://mlsanity.com/paper/2202.08485/) (2022)
- [An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters](https://mlsanity.com/paper/2303.12797/) (2023)
- [AutoDOViz: Human-Centered Automation for Decision Optimization](https://mlsanity.com/paper/2302.09688/) (2023)
- [FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting](https://mlsanity.com/paper/2306.10703/) (2023)
- [auto-sktime: Automated Time Series Forecasting](https://mlsanity.com/paper/2312.08528/) (2023)
- [Practical Forecasting of Cryptocoins Timeseries using Correlation Patterns](https://mlsanity.com/paper/2409.03674/) (2024)
- [LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing](https://mlsanity.com/paper/2305.12266/) (2023)
- [Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2403.04477/) (2024)
- [AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability](https://mlsanity.com/paper/2412.00419/) (2024)
- [TRACE-CASH: Trial-History-Conditioned Reinforcement Learning for Adaptive Configuration Exploration in Time-Series CASH](https://mlsanity.com/paper/2608.16410/) (2026)

## Abstract (arXiv)

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.