Results 271 to 280 of about 3,979,328 (298)

Efficient Shapelet Discovery for Time Series Classification

IEEE Transactions on Knowledge and Data Engineering, 2022
Time-series shapelets are discriminative subsequences, recently found effective for time series classification (tsc). It is evident that the quality of shapelets is crucial to the accuracy of tsc.
Guozhong Li, Byron Choi, Jianliang Xu, S. Bhowmick, Kwok-Pan Chun, Grace L.H. Wong   +5 more
semanticscholar   +1 more source

Analysis of different RNN autoencoder variants for time series classification and machine prognostics

, 2021
Recurrent neural network (RNN) based autoencoders, trained in an unsupervised manner, have been widely used to generate fixed-dimensional vector representations or embeddings for varying length multivariate time series.
W. Yu, I. Kim, C. Mechefske
semanticscholar   +1 more source

Scalable time series classification

Data Mining and Knowledge Discovery, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +1 more source

Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification

Neural Information Processing Systems
Medical time series (MedTS) data, such as Electroencephalography (EEG) and Electrocardiography (ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases.
Yihe Wang, Nan Huang, Taida Li, Yujun Yan, Xiang Zhang   +4 more
semanticscholar   +1 more source

Deep Learning For Time Series Classification Using New Hand-Crafted Convolution Filters

2022 IEEE International Conference on Big Data (Big Data), 2022
In recent years, there has been an increasing interest in Deep Learning models for time series classification. In this field, state-of-the-art architectures rely on convolution neural networks that learn one dimensional filters in order to capture ...
Ali Ismail-Fawaz, M. Devanne, J. Weber, G. Forestier   +3 more
semanticscholar   +1 more source

Genetic time series motif discovery for time series classification

International Journal of Biomedical Engineering and Technology, 2019
Time series is a sequence of continuous data and unbounded group of observations found in many applications. Time series motif discovery is an essential and important task in time series mining. Several algorithms have been proposed to discover motifs in time series.
E. Ramanujam, S. Padmavathi
openaire   +1 more source

Early classification on time series

Knowledge and Information Systems, 2011
In this paper, we formulate the problem of early classification of time series data, which is important in some time-sensitive applications such as health informatics. We introduce a novel concept of MPL (minimum prediction length) and develop ECTS (early classification on time series), an effective 1-nearest neighbor classification method.
Zhengzheng Xing, Jian Pei, Philip S. Yu
openaire   +1 more source

Explainable Multivariate Time Series Classification

Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021
Many real-world applications, e.g., healthcare, present multi-variate time series prediction problems. In such settings, in addition to the predictive accuracy of the models, model transparency and explainability are paramount. We consider the problem of building explainable classifiers from multi-variate time series data. A key criterion to understand
Tsung-Yu Hsieh, Suhang Wang, Yiwei Sun, Vasant Honavar   +3 more
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Efficient Classification of Long Time-Series

2013
Time-series classification has gained wide attention within the Machine Learning community, due to its large range of applicability varying from medical diagnosis, financial markets, up to shape and trajectory classification. The current state-of-art methods applied in time-series classification rely on detecting similar instances through neighboring ...
Grabocka J., Bedalli E., Schmidt-Thieme L.   +2 more
openaire   +1 more source

Semi-supervised time series classification

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006
The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data. In reality, such data may be very difficult or expensive to obtain. For example, it may require the time and expertise of cardiologists, space launch technicians, or other ...
Li Wei, Eamonn Keogh
openaire   +1 more source