Results 31 to 40 of about 424,943 (312)
Learning Disentangled Representations for Time Series
, 2021 Time-series representation learning is a fundamental task for time-series analysis. While significant progress has been made to achieve accurate representations for downstream applications, the learned representations often lack interpretability and do not expose semantic meanings.
Li, Yuening +6 more
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Yet Another Compact Time Series Data Representation Using CBOR Templates (YACTS)
Sensors, 2023 The Internet of Things (IoT) technology is growing rapidly, while the IoT devices are being deployed massively. However, interoperability with information systems remains a major challenge for this accelerated device deployment.
Sebastian Molina Araque +4 more
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Le pouvoir de la fiction télévisée d’un point de vue temporel
Communication, 2017 Whoever controls time exerts power, but the contemporary fragmentation of time makes it more difficult to control. Hence a sense of loss. The French short fiction television series Bref, beyond its comic content, offers the visceral experience of re ...
Jean-Bernard Cheymol
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A Novel Segmentation and Representation Approach for Streaming Time Series
IEEE Access, 2019 Along with the coming of Internet of Everything era, massive numbers of pervasive connected devices in various fields are continuously producing oceans of time series stream data.
Yupeng Hu +4 more
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Multivariate times series classification through an interpretable representation
Information Sciences, 2021 Multivariate time series classification is a task with increasing importance due to the proliferation of new problems in various fields (economy, health, energy, transport, crops, etc.) where a large number of information sources are available. Direct extrapolation of methods that traditionally worked in univariate environments cannot frequently be ...
Baldán Lozano, Francisco Javier +1 more
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Time Series Data Mining for Sport Data: a Review
International Journal of Computer Science in Sport, 2022 Time series data mining deals with extracting useful and meaningful information from time series data. Recently, the increasing use of temporal data, in particular time series data, has received much attention in the literature. Since most of sports data
Komitova Rumena +3 more
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Similarity Measurement and Classification of Temporal Data Based on Double Mean Representation
Algorithms, 2023 Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data.
Zhenwen He, Chi Zhang, Yunhui Cheng
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Sparse Representation Based Approach to Prediction for Economic Time Series
IEEE Access, 2019 This paper addresses the problem of economic time series forecasting, and a new prediction method is proposed. The method fully capitalizes on the two key technologies, sparse representation, and fuzzy set theory, to handle the stock time series ...
Weina Wang, Yanli Shi, Rong Luo
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Identifying the spectral representation of Hilbertian time series [PDF]
Statistics & Probability Letters, 2016 We provide square-root n consistency results regarding estimation of the spectral representation of covariance operators of Hilbertian time series, in a setting with imperfect measurements. This is a generalization of the method developed in Bathia et al. (2010).
Eduardo Horta, Flavio Ziegelmann
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IEEE Access, 2017 Data representation and similarity measurement are two basic aspects of similarity detection in time series data mining. In this paper, we present two novel approaches to perform similarity detection efficiently and effectively.
Miaomiao Zhang, Dechang Pi
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