Accurate and fast time series classification based on compressed random Shapelet Forest

Yang, Jun; Jing, Siyuan; Huang, Guanying

doi:10.1007/s10489-022-03852-2

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…The other six papers use ML after compression. In A7 [38] the authors use approximation techniques for data reduction and then use these reduced data in time series classification, applying Random Forest. In A12 [43], first two compression algorithms are investigated, then they are combined with eight ML algorithms, to identify the best performance in an agricultural fog environment.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Time Series Compression for IoT: A Systematic Literature Review

de Oliveira,

da Rocha,

Puntel

et al. 2023

Wireless Communications and Mobile Computing

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Time series are widely used to record information in applications developed with Internet of Things (IoT) devices, where sensors are used to collect large amounts of data. These devices, even with the exponential technological evolution of the last years, present limitations for the handling of this data given the enormous volume of data collected. Low storage or processing capacity and environments with restricted connection resources make it difficult to manipulate and transmit information. In this context, compression of information collected in the format of time series can be an alternative to reduce the amount of data handled by devices. However, given the peculiarities of IoT devices and applications, the compression process cannot be more costly than handling raw data. To identify the existing solutions to compress time series for IoT applications. In this paper, we present a descriptive systematic literature review on this topic. Based on a well-defined protocol, 40 papers were selected for review to analyze the strategies used, as well as performance and limitations. In this review, we aim to identify different approaches to solving the problem of data compression in the context addressed, as well as identifying directions for future research. The use of performance metrics in the reviewed papers was also reported in detail, to better understand how the authors compare their solutions to others. Additionally, the relationship between time series compression and machine learning (ML) techniques was highlighted. Being aware of the state-of-the-art in time series compression solutions for IoT can help us project future trends and challenges regarding this process and also to identify which algorithms, methods, and techniques can best be used in combination with ML models, the purpose inherent to the IoT context.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Time Series Compression for IoT: A Systematic Literature Review

de Oliveira,

da Rocha,

Puntel

et al. 2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Random forest (RF) algorithm is an ensemble learning algorithm based on decision trees, which has high accuracy, robustness, and scalability [18]. It is often used in prediction and classification fields.…”

Section: Introductionmentioning

confidence: 99%

Transformer fault diagnosis based on MPA-RF algorithm and LIF technology

Yan,

Wang,

Wang

et al. 2023

Meas. Sci. Technol.

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Power transformers are essential components in power systems used to regulate voltage, transmit electrical energy, provide isolation, and match loads. They contribute to efficient and reliable electricity transmission and distribution. However, traditional methods for diagnosing transformer faults are time-consuming, not suitable for online monitoring, and greatly affected by environmental conditions. In this experiment, we propose the use of laser-induced fluorescence (LIF) technology for transformer fault detection. LIF technology is a method for analyzing and detecting specific molecules or atoms in samples. It combines laser technology with fluorescence measurements, making it a powerful analytical tool. It achieves high sensitivity and selectivity in analyzing molecules and atoms by exciting and detecting fluorescence in the sample. This makes it an important technology in scientific research and practical applications. Furthermore, LIF technology has not been previously applied to power transformer fault diagnosis. Therefore, this experiment introduces a transformer fault diagnosis model based on the marine predators algorithm (MPA) optimized random forest (RF) algorithm and LIF spectroscopy technology. Four different oil samples were selected for experimentation: crude oil, thermally faulty oil, partially moist oil, and electrically faulty oil. First, LIF technology for collect spectral images and data from the different fault oil samples. The obtained spectral data was preprocessed using two methods, multivariate scatter correction (MSC) and standardization method (SNV). Then, principal component analysis (PCA) and kernel principal component analysis (KPCA) for reducing the dimensionality of the preprocessed spectral data. Finally, the RF model, MPA-RF model, and PSO-RF model were established; and the reduced data was input into the model for training. Through comparisons of the predictions on the test set, evaluation metrics of the algorithm (including fitting coefficient, MSE, RMSE, and RMSE), and iteration convergence curves, the best transformer fault diagnosis model was identified. The results show that the MSC-KPCA-MPA-RF model has the best matching resule, with a fitting coefficient of 0.9963 and a mean square error of 0.0047. The SNV-PCA-MPA-RF model has the worst fitting effect, with a fitting coefficient of 0.9840 and a mean square error of 0.0199. Through comparisons of the convergence of different models, the MSC-KPCA-MPA-RF model has the best convergence and is the most applicable model for transformer fault diagnosis in this experiment. This model has significant implications for ensuring the safety of the power system.

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Experimental analysis of similarity measurements for multivariate time series and its application to the stock market

Xiang

Wang

et al. 2023

Appl Intell

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Accurate and fast time series classification based on compressed random Shapelet Forest

Cited by 6 publications

References 33 publications

Time Series Compression for IoT: A Systematic Literature Review

Time Series Compression for IoT: A Systematic Literature Review

Transformer fault diagnosis based on MPA-RF algorithm and LIF technology

Experimental analysis of similarity measurements for multivariate time series and its application to the stock market

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