Multivariate time-series classification using memory and attention for long and short-term dependence$$^{\star }$$
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Multivariate time-series classification using memory and attention for long and short-term dependence$$^{\star }$$
Authors
Keywords
-
Journal
APPLIED INTELLIGENCE
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-11-03
DOI
10.1007/s10489-023-05079-1
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- FT-FVC: fast transformation-based feature vector concatenation for time series classification
- (2023) Changchun He et al. APPLIED INTELLIGENCE
- Parameterizing the cost function of dynamic time warping with application to time series classification
- (2023) Matthieu Herrmann et al. DATA MINING AND KNOWLEDGE DISCOVERY
- Multi-feature based network for multivariate time series classification
- (2023) Mingsen Du et al. INFORMATION SCIENCES
- Learning-based shapelets discovery by feature selection for time series classification
- (2022) Jiahui Chen et al. APPLIED INTELLIGENCE
- A deep learning framework for time series classification using normal cloud representation and convolutional neural network optimization
- (2022) El houssaine Hssayni et al. COMPUTATIONAL INTELLIGENCE
- KDCTime: Knowledge distillation with calibration on InceptionTime for time-series classification
- (2022) Xueyuan Gong et al. INFORMATION SCIENCES
- Multi-scale Attention Convolutional Neural Network for time series classification
- (2021) Wei Chen et al. NEURAL NETWORKS
- Early classification of time series based on trend segmentation and optimization cost function
- (2021) Wenjing Zhang et al. APPLIED INTELLIGENCE
- Convolutional Neural Network with an Elastic Matching Mechanism for Time Series Classification
- (2021) Kewei Ouyang et al. Algorithms
- Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification
- (2021) Yangqianhui Zhang et al. Applied Sciences-Basel
- XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
- (2021) Kevin Fauvel et al. Mathematics
- Fuzzy Cognitive Map-Driven Comprehensive Time-Series Classification
- (2021) Agnieszka Jastrzebska et al. IEEE Transactions on Cybernetics
- InceptionTime: Finding AlexNet for time series classification
- (2020) Hassan Ismail Fawaz et al. DATA MINING AND KNOWLEDGE DISCOVERY
- The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
- (2020) Alejandro Pasos Ruiz et al. DATA MINING AND KNOWLEDGE DISCOVERY
- Multivariate LSTM-FCNs for time series classification
- (2019) Fazle Karim et al. NEURAL NETWORKS
- Integration of residual network and convolutional neural network along with various activation functions and global pooling for time series classification
- (2019) Xiaowu Zou et al. NEUROCOMPUTING
- Multivariate comparison of classification performance measures
- (2018) Davide Ballabio et al. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
- Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges
- (2018) Henry Friday Nweke et al. EXPERT SYSTEMS WITH APPLICATIONS
- Capturing the Spatiotemporal Evolution in Road Traffic Networks
- (2018) Tarique Anwar et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Deep learning for sensor-based activity recognition: A Survey
- (2018) Jindong Wang et al. PATTERN RECOGNITION LETTERS
- Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model
- (2018) Wenjie Pei et al. IEEE Transactions on Neural Networks and Learning Systems
- Time Series Classification with Multivariate Convolutional Neural Network
- (2018) Chien-Liang Liu et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- A review of unsupervised feature learning and deep learning for time-series modeling
- (2014) Martin Längkvist et al. PATTERN RECOGNITION LETTERS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started