A review on outlier/anomaly detection in time series data
Recent advances in technology have brought major breakthroughs in data collection,
enabling a large amount of data to be gathered over time and thus generating time series …
enabling a large amount of data to be gathered over time and thus generating time series …
Deep learning for time series classification: a review
Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion
Unsupervised/self-supervised time series representation learning is a challenging problem
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …
A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
K Zhang, Q Wen, C Zhang, R Cai, M Jin… - … on Pattern Analysis …, 2024 - ieeexplore.ieee.org
Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
Anomaly detection based on convolutional recurrent autoencoder for IoT time series
C Yin, S Zhang, J Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the
technology of wireless and mobile communication. The data of target regions are collected …
technology of wireless and mobile communication. The data of target regions are collected …
Deep learning for time series classification and extrinsic regression: A current survey
N Mohammadi Foumani, L Miller, CW Tan… - ACM Computing …, 2024 - dl.acm.org
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
Spatio-temporal data mining: A survey of problems and methods
Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …
domains, including climate science, social sciences, neuroscience, epidemiology …
Deep learning for time-series analysis
JCB Gamboa - arXiv preprint arXiv:1701.01887, 2017 - arxiv.org
In many real-world application, eg, speech recognition or sleep stage classification, data are
captured over the course of time, constituting a Time-Series. Time-Series often contain …
captured over the course of time, constituting a Time-Series. Time-Series often contain …
A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction
Multivariate time series (MTS) prediction aims at predicting future time series by extracting
multiple forms of dependencies of past time series. Traditional prediction methods and deep …
multiple forms of dependencies of past time series. Traditional prediction methods and deep …