Results 41 to 50 of about 3,979,328 (298)
Probabilistic Imputation for Time-series Classification with Missing Data [PDF]
International Conference on Machine Learning, 2023 Multivariate time series data for real-world applications typically contain a significant amount of missing values. The dominant approach for classification with such missing values is to impute them heuristically with specific values (zero, mean, values
Seunghyun Kim +5 more
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Hydra: competing convolutional kernels for fast and accurate time series classification [PDF]
Data mining and knowledge discovery, 2022 We demonstrate a simple connection between dictionary methods for time series classification, which involve extracting and counting symbolic patterns in time series, and methods based on transforming input time series using convolutional kernels, namely ...
Angus Dempster +2 more
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Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions
IEEE Access, 2022 Time series data is increasingly used in a wide range of fields, and it is often relied on in crucial applications and high-stakes decision-making.
Andreas Theissler +3 more
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quant: a minimalist interval method for time series classification [PDF]
Data mining and knowledge discovery, 2023 We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and ...
Angus Dempster +2 more
semanticscholar +1 more source
Compound method of time series classification
Nonlinear Analysis, 2019 Many real phenomenona preserves the properties of chaotic dynamics. However, unambiguous determination of belonging to a group of chaotic systems is difficult and complex problem.
Łukasz Korus, Michał Piórek
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Classification of time series by shapelet transformation [PDF]
, 2014 Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A \emph{shapelet} is a time-series subsequence that allows for TSC based on local, phase-independent ...
Anthony Bagnall +23 more
core +1 more source
An Efficient Federated Distillation Learning System for Multitask Time Series Classification [PDF]
IEEE Transactions on Instrumentation and Measurement, 2021 This article proposes an efficient federated distillation learning system (EFDLS) for multitask time series classification (TSC). EFDLS consists of a central server and multiple mobile users, where different users may run different TSC tasks.
Huanlai Xing +4 more
semanticscholar +1 more source
Deep Multiple Metric Learning for Time Series Classification
IEEE Access, 2021 Effective distance metric plays an important role in time series classification. Metric learning, which aims to learn a data-adaptive distance metric to measure the distance among samples, has achieved promising results on time series classification ...
Zhi Chen +6 more
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Time Series Clustering and Classification [PDF]
, 2019 Questo libro contiene informazioni ottenute da fonti autentiche e apprezzate. Sono stati compiuti sforzi ragionevoli per pubblicare dati e informazioni affidabili, ma l'autore e l'editore non possono assumersi la responsabilità della validità di tutti i materiali o delle conseguenze del loro utilizzo.
E. A. Maharaj +2 more
openaire +2 more sources
Data mining and knowledge discovery, 2016 In the last 5 years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series ...
A. Bagnall +4 more
semanticscholar +1 more source