# A fast noise filtering algorithm for time series prediction using recurrent neural networks

arXiv: https://arxiv.org/abs/2007.08063 · HTML: https://mlsanity.com/paper/2007.08063/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

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

- [On a novel training algorithm for sequence-to-sequence predictive recurrent networks](https://mlsanity.com/paper/2106.14120/) (2021)
- [It's a super deal -- train recurrent network on noisy data and get smooth prediction free](https://mlsanity.com/paper/2206.04215/) (2022)

## Abstract (arXiv)

Recent research demonstrate that prediction of time series by recurrent neural networks (RNNs) based on the noisy input generates a smooth anticipated trajectory. We examine the internal dynamics of RNNs and establish a set of conditions required for such behavior. Based on this analysis we propose a new approximate algorithm and show that it significantly speeds up the predictive process without loss of accuracy.