Deep Learning for Time-Series Analysis

By John Cristian Borges Gamboa. 2017

476 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Economics and Econometrics, Signal Processing

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Citations in the corpus

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM (2018, background): “…prediction [Hochreiter and Schmidhuber, 1997; Gers, 2000; Yim, 2002; Zhang, 2005; Graves et al., 2009, 2013; Schmidhuber, 2015; Brownlee, 2016; Gamboa, 2017; Roondiwala, 2017] as well as its applications in economics and finance data such as predicting the volatility of the S&P 500 [Kohzadi,…”
Deep Air Quality Forecasting Using Hybrid Deep Learning Framework (2018, background): “Although air quality forecasting task usually adopts the traditional shallow machine learning methods, the deep learning method for time series analysis and air quality prediction is getting more and more attention from researchers [13] [14] [34].”
Time Series Data Augmentation for Deep Learning: A Survey (2020, background): “, 2019], and time series anomaly detection [Gamboa, 2017].”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, methodology): “Forecasting Machine Learning in Financial Crisis Prediction: A Survey [233] Deep Learning for Time-Series Analysis [234] A Survey on Machine Learning and Statistical Techniques in Bankruptcy Prediction [235]”
Deep Learning Based Forecasting of Critical Infrastructure Data (2017, background): “Since the vanishing gradient is the major drawback of RNNs [2], LSTM introduces a complex blocks of computing units with specific input gates to trap the errors without loosing the sequential properties of input data [15, 23].”
A Review of Deep Learning Models for Time Series Prediction (2019, methodology): “Regarding the above superiorities, deep learning-based models have been successfully applied in many ﬁelds pertinent to time series prediction, including remote sensing [11], multi-sensor fusion [12], [13], etc.”
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021, methodology): “After convolutional neural networks (CNN) revolutionized the ﬁeld of computer vision [31], researchers also began to apply CNN to time-series data analysis [32].”
TSMixer: An all-MLP Architecture for Time Series Forecasting (2023, methodology): “…Transformer-based models, have been proposed due to their capacity to capture both complex temporal patterns and cross-variate dependencies (Gamboa, 2017; Li et al., 2019; Wen et al., 2017; Zhou et al., 2021; Wu et al., 2021; Lim & Zohren, 2021; Liu et al., 2022a; Zhou et al., 2022b; Liu…”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, unknown): “[25] 2017 Time Series based DL Artificial Neural Networks, DL, Time-Series 3 2007/2017 60”
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL (2020, background): “Deep learning approaches for time series classification are covered in a variety of recent, excellent reviews [22]–[24].”