Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series

Li, Hailin; Lin, Chunpei; Wan, Xiaoji; Li, Zhengxin

doi:10.1109/access.2019.2915602

Cited by 10 publications

(11 citation statements)

References 31 publications

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“…Besides the variables selection and contribution rate, this study also trained data with only the variables considered most important. This could have been applied in the work Bannor and Acheampong (2019), as the selection of variables can identify a subset of the most relevant information in the system, and therefore influence the energy prediction accuracy (Yang et al, 2019;Peres and Fogliatto, 2018;Abedinia et al, 2016;Shafi et al, 2019;Li et al, 2019). The approach used in this work not only eliminates the variables that hinder the model, but also calculates the contribution rate for each variable.…”

Section: Resultsmentioning

confidence: 99%

“…Variable selection methods are fundamental because they can identify a subset of explanatory variables that contain the most relevant information in the complete data set and, therefore, influence the accuracy of the forecast (Yang et al , 2019; Peres and Fogliatto, 2018; Abedinia et al , 2016; Shafi et al , 2019; Li et al , 2019). Aware of this importance, some authors have explored the use of attribute selection techniques mainly to accelerate training and increase accuracy of forecasting models (Hafeez et al , 2021; Kim et al , 2020; Dai and Zhao, 2020).…”

Section: Related Workmentioning

confidence: 99%

“…Li et al (2019) proposed a resource representation and similarity measurement based on the covariance sequence for multivariate time series, in which the covariance between any two variables was applied to analyze the characteristics extraction for each multivariate time series. Thus, a covariance sequence can be constructed to represent the multivariate time series characteristic.…”

Section: Related Workmentioning

confidence: 99%

“…They used 311 classification data sets that were available free online and assessed prediction error rates, the number of variables, the computational time and the area under the receiver operating curve for many random selection methods of forest variables. Li et al (2019) proposed a resource representation and similarity measurement based on the covariance sequence for multivariate time series, in which the covariance between any two variables was applied to analyze the characteristics extraction for each multivariate time series. Thus, a covariance sequence can be constructed to represent the multivariate time series characteristic.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

A PCA-based variable ranking and selection approach for electric energy load forecasting

Bezerra

Grassi

Dias

et al. 2022

IJESM

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Purpose This paper aims to propose an approach based upon the principal component analysis (PCA) to define a contribution rate for each variable and then select the main variables as inputs to a neural network for energy load forecasting in the region southeastern Brazil. Design/methodology/approach The proposed approach defines a contribution rate of each variable as a weighted sum of the inner product between the variable and each principal component. So, the contribution rate is used for selecting the most important features of 27 variables and 6,815 electricity data for a multilayer perceptron network backpropagation prediction model. Several tests, starting from the most significant variable as input, and adding the next most significant variable and so on, are accomplished to predict energy load (GWh). The Kaiser–Meyer–Olkin and Bartlett sphericity tests were used to verify the overall consistency of the data for factor analysis. Findings Although energy load forecasting is an area for which databases with tens or hundreds of variables are available, the approach could select only six variables that contribute more than 85% for the model. While the contribution rates of the variables of the plants, plus energy exchange added, have only 14.14% of contribution, the variable the stored energy has a contribution rate of 26.31% being fundamental for the prediction accuracy. Originality/value Besides improving the forecasting accuracy and providing a faster predictor, the proposed PCA-based approach for calculating the contribution rate of input variables providing a better understanding of the underlying process that generated the data, which is fundamental to the Brazilian reality due to the accentuated climatic and economic variations.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A PCA-based variable ranking and selection approach for electric energy load forecasting

Bezerra

Grassi

Dias

et al. 2022

IJESM

View full text Add to dashboard Cite

show abstract

“…With respect to various applications, the distance can be defined in different ways. There are many methods that can be used to measure the distance between time series, such as the Euclidean distance [19], the dynamic time warping distance [20][21], the longest common string [16], and the edit distance [22][23]. Among them, the edit distance is a measurement of the distance between the two string sequences.…”

Section: Calculation Of Time Series Similaritymentioning

confidence: 99%

Fault diagnosis and identification of malfunctioning protection devices in a power system via time series similarity matching

Wang

Wen

et al. 2020

Energy Conversion and Economics

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Alarm messages uploaded to a dispatch centre following the failure of a power system contain extensive temporal information. The accuracy and speed of fault diagnosis can be improved upon taking full advantage of such temporal information contained in these messages. From this standpoint, a power system fault diagnosis model based on time series similarity matching is proposed herein. First, a set of suspected faulty components can be determined after the occurrence of a fault or a set of faults. Then, on the basis of the specifications of protection devices, including protective relays and circuit breakers, and the defined time series model, a set of alarm hypothesis time series is generated, containing action process information of the protection devices involved in these suspicious components. Meanwhile, the alarm messages received by the dispatch centre are pre-processed, and the alarm information time series are obtained. Subsequently, the fault component is obtained by calculating the similarity between each series in the alarm hypothesis time series set and the alarm information time series. Finally, the correctness and effectiveness of the proposed fault diagnosis model are demonstrated via a real-life scenario in a local power system in China.

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A two-step passenger flow anomaly detection scheme based on machine learning methods

Shuai,

Ruan,

Ouyang

et al. 2024

Discov Appl Sci

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Subway is an important transportation means for residents due to its large volume, punctuality and environmental friendliness. However, weather factors, sports events, concerts and some unexpected events can lead to a surge or abnormality in passenger flow, which brings enormous pressure to the management of stations and passenger flow guidance. Inspired by this, this paper formulates the abnormal passenger flows into different categories in terms of the characteristics and periodical trends, and proposes a two-step abnormal detection scheme to identify the anomalies and their type, and locate abnormal positions. First, two abnormal passenger flows recognition methods based on Jensen–Shannon divergence, dynamic time warping, and density-based spatial clustering of applications with noise are established to identify the station-level abnormal passenger flow. Then, a triple standard deviation algorithm based on sliding window is further proposed to identify the abnormal type and position. Real-world smart card data of the Beijing subway in China, and the manual mutation data of the real data are employed to evaluate effectiveness of our framework. The results show that our two-step scheme is superior to the state-of-the-art algorithms, which can detect out and locate abnormal passenger flows with various characteristics. On more mutation data, this paper discusses the performances on various anomalies of different types of stations in depth, which further indicates our framework is robust and effective in practice.

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Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series

Cited by 10 publications

References 31 publications

A PCA-based variable ranking and selection approach for electric energy load forecasting

A PCA-based variable ranking and selection approach for electric energy load forecasting

Fault diagnosis and identification of malfunctioning protection devices in a power system via time series similarity matching

A two-step passenger flow anomaly detection scheme based on machine learning methods

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