A Feature Extraction Method for Multivariate Time Series Classification Using Temporal Patterns

Zhou, Pei-Yuan; Chan, Kwong

doi:10.1007/978-3-319-18032-8_32

Cited by 13 publications

(4 citation statements)

References 19 publications

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“…After studying the sea clutter, Yokoyama et al (2015) found that sea clutter was also chaotic. This discovery has strongly impacted the traditional signal detection theory to cross chaos theory with other disciplines, making the application of chaos theory further (Zhou and Chan 2015). The development, for example, combines chaos theory with information processing.…”

Section: Related Workmentioning

confidence: 99%

Human-autonomous devices for weak signal detection method based on multimedia chaos theory

Zhu

Lin

et al. 2020

J Ambient Intell Human Comput

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The detection of weak signals has been widely used in communication, radar and other fields. The detection of weak signals in the background of strong noise is an important research hotspot of modern information theory, and prompts people to explore and study new theories and new methods of weak signal detection. The device takes Holmes-type Duffing mapping as the research object, and uses the Lyapunov exponent as the criterion for chaos identification. The chaotic critical value of the equation is changed from chaotic state to periodic state. Whether it contains the detection algorithm of the target signal, the signal detection for the unknown frequency and the Holmes-type Duffing system is improved by the sliding mode variable structure control method in the control theory. The simulation results show that the improved chaotic Duffing system can effectively suppress the noise and detect the frequency of the weak signal through the power spectrum of the system.

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Section: Related Workmentioning

confidence: 99%

Human-autonomous devices for weak signal detection method based on multimedia chaos theory

Zhu

Lin

et al. 2020

J Ambient Intell Human Comput

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“…At present, the sequence in sequence classification is a time series. Every sequence associates a label, and every sequence have a fixed length in order to be available to a specified classifier [13]. Therefore, the fault diagnosis of bearings using vibration signal can be categorized to sequence classification through Naïve Bayes classifier.…”

Section: Sequence Classificationmentioning

confidence: 99%

Fault diagnosis of rolling element bearing using Naïve Bayes classifier

Yi¹,

Chen²

2017

Vib. proced.

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Abstract. The development of machine learning brings a new way for diagnosing the fault of rolling element bearings. However, the method in machine learning with high accuracy often has the poor ability of generalization due to the overuse of feature engineering. To address this challenge, Naïve Bayes classifier is applied in this paper. As the one of the cluster of Bayes classifiers, its ability of classification is very outstanding. In this paper, the method is provided with a detailed description for why and how to diagnose the fault of bearing. Finally, an evaluation of the performance of Naïve Bayes classifier is presented with real world data. The evaluation indicates that Naïve Bayes classifier can achieve a high level of accuracy without any feature engineering.

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“…These large-scale data encode important information about complex relations among individual time series. Recent works on multivariate time-series (MTS) pattern discovery focus mainly on extracting temporal association patterns and features [1][2][3]. Many of these MTS are spatio-temporal by nature in which they are collected together with location information such as latitude and longitude.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Spatio-Temporal Patterns in Multivariate Spatial Time Series

Chan

2020

ACM/IMS Trans. Data Sci.

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With the advancement of the computing technology and its wide range of applications, collecting large sets of multivariate time series in multiple geographical locations introduces a problem of identifying interesting spatio-temporal patterns. We consider a new spatial structure of the data in the pattern discovery process due to the dependent nature of the data. This article presents an information-theoretic approach to detect the temporal patterns from the multivariate time series in multiple locations. Based on their occurrences of discovered temporal patterns, we propose a method to identify interesting spatio-temporal patterns by a statistical significance test. Furthermore, the identified spatio-temporal patterns can be used for clustering and classification. For evaluating the performance, a simulated dataset is tested to validate the quality of the identified patterns and compare with other approaches. The result indicates the approach can effectively identify useful patterns to characterize the dataset for further analysis in achieving good clustering quality. Furthermore, experiments on real-world datasets and case studies have been conducted to illustrate the applicability and the practicability of the proposed approach. CCS Concepts: • Mathematics of computing → Time series analysis; Cluster analysis; • Information systems → Data mining;

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A Feature Extraction Method for Multivariate Time Series Classification Using Temporal Patterns

Cited by 13 publications

References 19 publications

Human-autonomous devices for weak signal detection method based on multimedia chaos theory

Human-autonomous devices for weak signal detection method based on multimedia chaos theory

Fault diagnosis of rolling element bearing using Naïve Bayes classifier

Discovery of Spatio-Temporal Patterns in Multivariate Spatial Time Series

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