[Paper Review] Deep Learning for Time-Series Analysis

John Cristian Borges Gamboa|arXiv (Cornell University)|Jan 7, 2017

Time Series Analysis and Forecasting44 references161 citations

TL;DR

This paper surveys deep learning methods for time-series tasks, covering modeling, classification, and anomaly detection, with examples like UFCNN, GAF/MTF image representations, and stacked LSTM approaches.

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.

Motivation & Objective

• Motivate the use of deep learning for time-series data with temporal dependencies beyond hand-crafted features.
• Review core neural architectures (CNNs, RNNs, LSTMs) and their adaptations for time-series.
• Present methods for time-series modeling, classification, and anomaly detection.
• Discuss how deep learning can improve forecasting and pattern recognition in time-series contexts.

Proposed method

• Describe the basic ANN modules and how CNNs and RNNs (including LSTMs) are applied to time-series.
• Introduce specialized architectures for time-series, such as UFCNN (Undecimated FCN) and its translation-invariance properties.
• Explain transforming time-series into images via Gramian Angular Fields (GAF) and Markov Transition Fields (MTF) for CNN-based classification.
• Discuss anomaly detection using stacked LSTM architectures and Gaussian-based probabilistic scoring.
• Provide examples comparing UFCNNs to RNN/LSTM baselines across forecasting and trading-like datasets.

Experimental results

Research questions

• RQ1 What deep learning architectures are effective for modeling time-series data with temporal dependencies?
• RQ2 How do image-based representations (GAF/MTF) enable CNN-based time-series classification?
• RQ3 Can specialized architectures like UFCNN improve forecasting and anomaly detection compared to traditional RNNs/LSTMs?
• RQ4 What role do deep learning methods play in time-series anomaly detection and forecasting across diverse domains?
• RQ5 How do deep learning approaches compare to traditional feature-engineering methods in time-series tasks?

Key findings

• UFCNNs can outperform RNNs and LSTMs on several time-series forecasting tasks in the reported experiments.
• CNN-based classification using time-series–to-image representations (GAF/MTF) yields competitive results on hard datasets.
• Transforming time-series into multi-channel images enables effective use of tiled CNNs for classification.
• Stacked LSTM architectures improve anomaly detection by modeling long-range dependencies and estimating multivariate probabilities.
• Deep learning approaches show promise across modeling, classification, and anomaly detection tasks compared to traditional methods.