# An Attention Free Long Short-Term Memory for Time Series Forecasting

arXiv: https://arxiv.org/abs/2209.09548 · HTML: https://mlsanity.com/paper/2209.09548/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [An Attention Free Transformer](https://mlsanity.com/paper/2105.14103/) (2021)

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

- [Clustering Digital Assets Using Path Signatures: Application to Portfolio Construction](https://mlsanity.com/paper/2410.23297/) (2024)
- [An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies](https://mlsanity.com/paper/2304.10614/) (2023)
- [Knowledge-enhanced Transformer for Multivariate Long Sequence Time-series Forecasting](https://mlsanity.com/paper/2411.11046/) (2024)

## Abstract (arXiv)

Deep learning is playing an increasingly important role in time series analysis. We focused on time series forecasting using attention free mechanism, a more efficient framework, and proposed a new architecture for time series prediction for which linear models seem to be unable to capture the time dependence. We proposed an architecture built using attention free LSTM layers that overcome linear models for conditional variance prediction. Our findings confirm the validity of our model, which also allowed to improve the prediction capacity of a LSTM, while improving the efficiency of the learning task.