# Forecasting time series with encoder-decoder neural networks

arXiv: https://arxiv.org/abs/2009.08848 · HTML: https://mlsanity.com/paper/2009.08848/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

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

- [Adaptive deep learning for nonlinear time series models](https://mlsanity.com/paper/2207.02546/) (2022)
- [Time Series Analysis and Modeling to Forecast: a Survey](https://mlsanity.com/paper/2104.00164/) (2021)

## Abstract (arXiv)

In this paper, we consider high-dimensional stationary processes where a new observation is generated from a compressed version of past observations. The specific evolution is modeled by an encoder-decoder structure. We estimate the evolution with an encoder-decoder neural network and give upper bounds for the expected forecast error under specific structural and sparsity assumptions. The results are shown separately for conditions either on the absolutely regular mixing coefficients or the functional dependence measure of the observed process. In a quantitative simulation we discuss the behavior of the network estimator under different model assumptions. We corroborate our theory by a real data example where we consider forecasting temperature data.