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

TL;DR

This paper introduces an attention-free LSTM architecture for time series forecasting that enhances prediction accuracy and efficiency over traditional linear models and standard LSTMs.

Contribution

The paper presents a novel attention-free LSTM architecture specifically designed for time series prediction, improving over existing models in capturing temporal dependencies.

Findings

The proposed model outperforms linear models in capturing time dependence.

It improves the prediction capacity of standard LSTM models.

The architecture enhances learning efficiency.

Abstract

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.