# Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model

arXiv: https://arxiv.org/abs/2212.07918 · HTML: https://mlsanity.com/paper/2212.07918/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)
- [Quantifying Uncertainties in Fault Slip Distribution during the T=ohoku Tsunami using Polynomial Chaos](https://mlsanity.com/paper/1607.07414/) (2016)

## Built on by (most cited first, 1 of 1)

- [Interpretable Physics Reasoning and Performance Taxonomy in Vision-Language Models](https://mlsanity.com/paper/2509.08270/) (2025)

## Abstract (arXiv)

Recent developments of advanced driver-assistance systems necessitate an increasing number of tests to validate new technologies. These tests cannot be carried out on track in a reasonable amount of time and automotive groups rely on simulators to perform most tests. The reliability of these simulators for constantly refined tasks is becoming an issue and, to increase the number of tests, the industry is now developing surrogate models, that should mimic the behavior of the simulator while being much faster to run on specific tasks. In this paper we aim to construct a surrogate model to mimic and replace the simulator. We first test several classical methods such as random forests, ridge regression or convolutional neural networks. Then we build three hybrid models that use all these methods and combine them to obtain an efficient hybrid surrogate model.