# Combining Multiple Time Series Models Through A Robust Weighted Mechanism

arXiv: https://arxiv.org/abs/1302.6595 · HTML: https://mlsanity.com/paper/1302.6595/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2013.

## Built on by (most cited first, 3 of 3)

- [Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach](https://mlsanity.com/paper/2304.04308/) (2023)
- [Multi-layer Stack Ensembles for Time Series Forecasting](https://mlsanity.com/paper/2511.15350/) (2025)
- [TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting](https://mlsanity.com/paper/2605.09208/) (2026)

## Abstract (arXiv)

Improvement of time series forecasting accuracy through combining multiple models is an important as well as a dynamic area of research. As a result, various forecasts combination methods have been developed in literature. However, most of them are based on simple linear ensemble strategies and hence ignore the possible relationships between two or more participating models. In this paper, we propose a robust weighted nonlinear ensemble technique which considers the individual forecasts from different models as well as the correlations among them while combining. The proposed ensemble is constructed using three well-known forecasting models and is tested for three real-world time series. A comparison is made among the proposed scheme and three other widely used linear combination methods, in terms of the obtained forecast errors. This comparison shows that our ensemble scheme provides significantly lower forecast errors than each individual model as well as each of the four linear combination methods.