# Review of automated time series forecasting pipelines

arXiv: https://arxiv.org/abs/2202.01712 · HTML: https://mlsanity.com/paper/2202.01712/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 149 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Best Subset Selection via a Modern Optimization Lens](https://mlsanity.com/paper/1507.03133/) (2015)
- [Machine Learning Advances for Time Series Forecasting](https://mlsanity.com/paper/2012.12802/) (2020)
- [Forecasting: theory and practice](https://mlsanity.com/paper/2012.03854/) (2020)
- [Forecasting with time series imaging](https://mlsanity.com/paper/1904.08064/) (2019)
- [VEST: Automatic Feature Engineering for Forecasting](https://mlsanity.com/paper/2010.07137/) (2020)
- [Automated Machine Learning in Practice: State of the Art and Recent Results](https://mlsanity.com/paper/1907.08392/) (2019)
- [pyWATTS: Python Workflow Automation Tool for Time Series](https://mlsanity.com/paper/2106.10157/) (2021)
- [Time Series Data Cleaning with Regular and Irregular Time Intervals](https://mlsanity.com/paper/2004.08284/) (2020)
- [Data-Driven Copy-Paste Imputation for Energy Time Series](https://mlsanity.com/paper/2101.01423/) (2021)
- [Concepts for Automated Machine Learning in Smart Grid Applications](https://mlsanity.com/paper/2110.13585/) (2021)
- [Semi-automated simultaneous predictor selection for Regression-SARIMA models](https://mlsanity.com/paper/2001.02883/) (2020)

## Built on by (most cited first, 18 of 18)

- [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2308.05566/) (2023)
- [Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022](https://mlsanity.com/paper/2305.04811/) (2023)
- [Efficient Automated Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2205.05511/) (2022)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [Feature Programming for Multivariate Time Series Prediction](https://mlsanity.com/paper/2306.06252/) (2023)
- [Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting](https://mlsanity.com/paper/2212.07771/) (2022)
- [auto-sktime: Automated Time Series Forecasting](https://mlsanity.com/paper/2312.08528/) (2023)
- [A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models](https://mlsanity.com/paper/2501.01394/) (2025)
- [A Survey of Open Source Automation Tools for Data Science Predictions](https://mlsanity.com/paper/2208.11792/) (2022)
- [AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models](https://mlsanity.com/paper/2212.06797/) (2022)
- [QxEAI: Quantum-like evolutionary algorithm for automated probabilistic forecasting](https://mlsanity.com/paper/2405.03701/) (2024)
- [AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability](https://mlsanity.com/paper/2412.00419/) (2024)
- [On autoregressive deep learning models for day-ahead wind power forecasting with irregular shutdowns due to redispatching](https://mlsanity.com/paper/2412.00423/) (2024)
- [Data-driven Lake Water Quality Forecasting for Time Series with Missing Data using Machine Learning](https://mlsanity.com/paper/2601.15503/) (2026)
- [Chronax: A Jax Library for Forecasting and Conformal Inference](https://mlsanity.com/paper/2604.16719/) (2026)
- [Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty](https://mlsanity.com/paper/2607.12730/) (2026)
- [TRACE-CASH: Trial-History-Conditioned Reinforcement Learning for Adaptive Configuration Exploration in Time-Series CASH](https://mlsanity.com/paper/2608.16410/) (2026)
- [On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models](https://mlsanity.com/paper/2610.13676/) (2026)

## Abstract (arXiv)

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting.