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Target Class Supervised Sample Length and Training Sample Reduction of Univariate Time Series

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Advances and Trends in Artificial Intelligence. From Theory to Practice (IEA/AIE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12799))

Abstract

The anomaly/novelty detection in time series data analysis is one of the most admired area of research, which is specifically a one-class classification problem. Concerning univariate time series data, apart from a huge number of training samples the higher sample span (observation length) also adds computation overhead along with its intrinsic issue of curse of dimensionality (sample length is considered as dimension). In this context, the present research proposes a concurrent way of sample span (treated as dimension) and training sample reduction approach for univariate time series data under the supervision of target class samples. Data representation of time series decides the performance of any machine learning approach, therefore the present research utilizes dissimilarity-based representation (DBR) techniques for time series data representation and later to reduce the sample length, a knowledge grid is computed via eigen space analysis of variance-covariance of target class samples. This knowledge grid is further used to transform the original sample length to reduced one. Afterwards, the training samples are selected using prototype methods. For experiments 16 different DBR measures are used along with 11 prototype techniques. Finally, one-class support vector machine (OCSVM) and 1-nearest neighbour (1-NN) are utilized for classification to validate the performance of proposed approach over 85 UCR/UEA univariate datasets.

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Authors and Affiliations

  1. IIIT Allahabad, Prayagraj, 211015, India

    Sanjay Kumar Sonbhadra, Sonali Agarwal & P. Nagabhushan

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  1. Sanjay Kumar Sonbhadra
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  2. Sonali Agarwal
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  3. P. Nagabhushan
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Corresponding author

Correspondence to Sanjay Kumar Sonbhadra .

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Editors and Affiliations

  1. i-SOMET Incorporate Association, Morioka, Japan

    Hamido Fujita

  2. Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  4. Texas State University San Marcos, San Marcos, TX, USA

    Moonis Ali

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Sonbhadra, S.K., Agarwal, S., Nagabhushan, P. (2021). Target Class Supervised Sample Length and Training Sample Reduction of Univariate Time Series. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_51

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  • DOI: https://doi.org/10.1007/978-3-030-79463-7_51

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