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Deep learning for time series classification and extrinsic regression: A current survey
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 …
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 …
Multi-input CNN-GRU based human activity recognition using wearable sensors
Abstract Human Activity Recognition (HAR) has attracted much attention from researchers in
the recent past. The intensification of research into HAR lies in the motive to understand …
the recent past. The intensification of research into HAR lies in the motive to understand …
Wearable sensor-based human activity recognition with transformer model
Computing devices that can recognize various human activities or movements can be used
to assist people in healthcare, sports, or human–robot interaction. Readily available data for …
to assist people in healthcare, sports, or human–robot interaction. Readily available data for …
Deep learning for time series classification
Time series analysis is a field of data science which is interested in analyzing sequences of
numerical values ordered in time. Time series are particularly interesting because they allow …
numerical values ordered in time. Time series are particularly interesting because they allow …
A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems
Fault diagnosis plays an important role in actual production activities. As large amounts of
data can be collected efficiently and economically, data-driven methods based on deep …
data can be collected efficiently and economically, data-driven methods based on deep …
A hybrid attention-based deep learning approach for wind power prediction
Renewable energy, especially wind power, is a practicable and promising solution to
mitigate the existing dilemma associated with climate change. Efficient and accurate …
mitigate the existing dilemma associated with climate change. Efficient and accurate …
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 …
Fully-connected spatial-temporal graph for multivariate time-series data
Multivariate Time-Series (MTS) data is crucial in various application fields. With its
sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits …
sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits …
Deep learning in smart grid technology: A review of recent advancements and future prospects
The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …
a promising landscape for high grid reliability and efficient energy management. This …