Deep Learning for Time-Series Analysis

TL;DR

This paper reviews how deep learning techniques are transforming time-series analysis by automating feature extraction and improving forecasting accuracy across various real-world applications.

Contribution

It provides a comprehensive overview of deep learning methods applied to time-series analysis and summarizes recent applications demonstrating their effectiveness.

Findings

Deep learning models outperform traditional methods in time-series forecasting.

Automated feature learning reduces reliance on handcrafted features.

Deep learning enhances accuracy in applications like speech recognition and sleep stage classification.

Abstract

In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identical points of time to belong to different classes or predict different behavior. This characteristic generally increases the difficulty of analysing them. Existing techniques often depended on hand-crafted features that were expensive to create and required expert knowledge of the field. With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. Such new developments are the topic of this paper: a review of the main Deep Learning techniques is presented, and some applications on Time-Series analysis are summaried. The results make it clear that Deep Learning has a lot to contribute to the field.