# Forecasting large collections of time series: feature-based methods

arXiv: https://arxiv.org/abs/2309.13807 · HTML: https://mlsanity.com/paper/2309.13807/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [GRATIS: GeneRAting TIme Series with diverse and controllable characteristics](https://mlsanity.com/paper/1903.02787/) (2019)
- [Highly comparative time-series analysis: The empirical structure of time series and their methods](https://mlsanity.com/paper/1304.1209/) (2013)
- [Forecasting with time series imaging](https://mlsanity.com/paper/1904.08064/) (2019)
- [Forecast with Forecasts: Diversity Matters](https://mlsanity.com/paper/2012.01643/) (2020)
- [Another look at forecast trimming for combinations: robustness, accuracy and diversity](https://mlsanity.com/paper/2208.00139/) (2022)
- [Exploring the representativeness of the M5 competition data](https://mlsanity.com/paper/2103.02941/) (2021)
- [Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings](https://mlsanity.com/paper/1703.09480/) (2017)

## Abstract (arXiv)

In economics and many other forecasting domains, the real world problems are too complex for a single model that assumes a specific data generation process. The forecasting performance of different methods changes depending on the nature of the time series. When forecasting large collections of time series, two lines of approaches have been developed using time series features, namely feature-based model selection and feature-based model combination. This chapter discusses the state-of-the-art feature-based methods, with reference to open-source software implementations.