# Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast

arXiv: https://arxiv.org/abs/2306.07119 · HTML: https://mlsanity.com/paper/2306.07119/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality](https://mlsanity.com/paper/2008.00444/) (2020)
- [Global Models for Time Series Forecasting: A Simulation Study](https://mlsanity.com/paper/2012.12485/) (2020)
- [Ensembles of Localised Models for Time Series Forecasting](https://mlsanity.com/paper/2012.15059/) (2020)
- [k*-Nearest Neighbors: From Global to Local](https://mlsanity.com/paper/1701.07266/) (2017)

## Built on by (most cited first, 2 of 2)

- [Multi-Modal Time Series Prediction via Mixture of Modulated Experts](https://mlsanity.com/paper/2601.21547/) (2026)
- [Two-stage hybrid models for enhancing forecasting accuracy on heterogeneous time series](https://mlsanity.com/paper/2502.08600/) (2025)

## Abstract (arXiv)

A common forecasting setting in real world applications considers a set of possibly heterogeneous time series of the same domain. Due to different properties of each time series such as length, obtaining forecasts for each individual time series in a straight-forward way is challenging. This paper proposes a general framework utilizing a similarity measure in Dynamic Time Warping to find similar time series to build neighborhoods in a k-Nearest Neighbor fashion, and improve forecasts of possibly simple models by averaging. Several ways of performing the averaging are suggested, and theoretical arguments underline the usefulness of averaging for forecasting. Additionally, diagnostics tools are proposed allowing a deep understanding of the procedure.