Exploring interval implicitization in real-valued time series classification and its applications

Due to the fact of uncertainty contained in observed real-valued time series, the aim of this paper is to explore interval implicitization in real-valued time series classification problems. A novel real-valued time series classification method under the transformed implicit interval-valued data env...

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Vydáno v:The Journal of supercomputing Ročník 79; číslo 3; s. 3373 - 3391
Hlavní autoři: Tao, Zhifu, Yao, Bingxin, Zhu, Jiaming
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.02.2023
Springer Nature B.V
Témata:
Algorithms
Classification
Compilers
Computer Science
Datasets
Decomposition
Interpreters
Methods
Neural networks
Processor Architectures
Programming Languages
Time series
Interval-valued time series
ARIMA
Dynamic time warping
Time series
Classification
ISSN:0920-8542, 1573-0484
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Shrnutí:Due to the fact of uncertainty contained in observed real-valued time series, the aim of this paper is to explore interval implicitization in real-valued time series classification problems. A novel real-valued time series classification method under the transformed implicit interval-valued data environment is developed, namely 1NN-IDTW. To do this, by utilizing the ARIMA model, real-valued time series are first converted in parallel to interval-valued time series. Then, the integration of explored interval implicitization process, Dynamic Time Warping algorithm and the simple nearest neighbor classifier is proposed. In the numerical experimental part, the developed 1NN-IDTW is first directly applied to randomly selected 16 real-world datasets from the UCR time series archive for time series classification. The explored interval implicitization process is also integrated with different classification models, so as to verity its performance. The results indicate that our developed model performs better on 13 datasets over 6 baselines. Furthermore, comparing with existed time series classification methods, the integration of interval implicitization can improve the prediction accuracy by more than 10%.
Bibliografie:ObjectType-Article-1
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04792-x