Results 31 to 40 of about 655,816 (301)
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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Nonparametric frequency domain analysis of nonstationary multivariate time series [PDF]
, 2002 We analyse the properties of nonparametric spectral estimates when applied to long memory and trending nonstationary multiple time series. We show that they estimate consistently a generalized or pseudo-spectral density matrix at frequencies both close ...
Velasco Gómez, Carlos +2 more
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HyperTime: Implicit Neural Representation for Time Series
CoRR, 2022 Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. Their robustness as general approximators has been shown in a wide variety of data sources, with applications on image, sound, and 3D scene representation.
Elizabeth Fons +4 more
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A Novel Time Series Representation Approach for Dimensionality Reduction [PDF]
, 2022 With the growth of streaming data from many domains such as transportation, finance, weather, etc, there has been a surge in interest in time series data mining.
Mohammad Bawaneh +3 more
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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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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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Characteristic Representation of Stock Time Series Based on Trend Feature Points
IEEE Access, 2020 Stocks are the most active part of the securities market, and the analysis of stock generally starts from the price fluctuation. Stock trading data have the characteristics of time series, which make it possible to record the transaction prices in a time-
Mengna Zhou +3 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background An internal tandem duplication in the gene encoding Fms‐like tyrosine kinase 3 (FLT3‐ITD) is associated with high relapse risk and poor prognosis in acute myeloid leukemia (AML) and plays a crucial role in treatment decisions. Measurable residual disease (MRD) analysis of FLT3‐ITD during and after treatment has shown prognostic ...
Sofie Johansson Alm +11 more
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Applied Sciences, 2023 Contrastive learning, as an unsupervised technique, has emerged as a prominent method in time series representation learning tasks, serving as a viable solution to the scarcity of annotated data.
Yubo Zheng +4 more
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