Data Augmentation with Suboptimal Warping for Time-Series Classification [PDF]
Sensors, 2019 In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths.
Krzysztof Kamycki +2 more
doaj +2 more sources
An empirical survey of data augmentation for time series classification with neural networks. [PDF]
PLoS One, 2021 In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization.
Iwana BK, Uchida S.
europepmc +3 more sources
HIVE-COTE 2.0: a new meta ensemble for time series classification [PDF]
Machine-mediated learning, 2021 The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets, bag ...
Matthew Middlehurst +5 more
semanticscholar +1 more source
Bake off redux: a review and experimental evaluation of recent time series classification algorithms [PDF]
Data mining and knowledge discovery, 2023 In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31(3):606-660. 2017) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive.
Matthew Middlehurst +2 more
semanticscholar +1 more source
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey [PDF]
ACM Computing Surveys, 2023 Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series ...
Navid Mohammadi Foumani +5 more
semanticscholar +1 more source
Improving position encoding of transformers for multivariate time series classification [PDF]
Data mining and knowledge discovery, 2023 Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to capture the ordering of the time series data.
Navid Mohammadi Foumani +3 more
semanticscholar +1 more source
TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification [PDF]
Information Sciences, 2023 Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology. Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different ...
Huaiyuan Liu +6 more
semanticscholar +1 more source
Time Series Classification with Shapelet and Canonical Features
Applied Sciences, 2022 Shapelet-based time series classification methods are widely adopted models for time series classification tasks. However, the high computational cost greatly limits the practicability of the Shapelet-based methods. What is more, traditional Shapelet can
Hai-Yang Liu +3 more
doaj +1 more source
LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation [PDF]
Proc. ACM Manag. Data, 2023 Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision making in multiple domains.
David Campos +5 more
semanticscholar +1 more source
Time series classification from scratch with deep neural networks: A strong baseline [PDF]
IEEE International Joint Conference on Neural Network, 2016 We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting.
Zhiguang Wang, Weizhong Yan, T. Oates
semanticscholar +1 more source