A novel trend based SAX reduction technique for time series

Yahyaoui, Hamdi; Aldaihani, Reem

doi:10.1016/j.eswa.2019.04.026

Cited by 30 publications

(11 citation statements)

References 10 publications

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“…A short overview of different smoothing methods can be found in [19]. Yahyaoui and Al-Daihani [20] suggested a novel Symbolic Aggregate approXimation (SAX) and compared it to the standards, obtaining better results for time series classification. Smoothing techniques also include regressions and clustering.…”

Section: Data Transformationmentioning

confidence: 99%

Predictive Maintenance on the Machining Process and Machine Tool

Jimenez-Cortadi¹,

Irigoien²,

Boto

et al. 2019

Applied Sciences

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This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces.

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Section: Data Transformationmentioning

confidence: 99%

Predictive Maintenance on the Machining Process and Machine Tool

Jimenez-Cortadi¹,

Irigoien²,

Boto

et al. 2019

Applied Sciences

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“…The existing time series classification methods mostly use symbolic aggregation approximation (SAX) 6 and convolution neural network (CNN) 7 , 8 , but ignore the time attribute and the classification accuracy is not high. Therefore, to solve the above problems, this paper proposes a T-CNN time series classification method based on the Gram matrix 9 .…”

Section: Introductionmentioning

confidence: 99%

A T-CNN time series classification method based on Gram matrix

Wang

et al. 2022

Sci Rep

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Time series classification is a basic task in the field of streaming data event analysis and data mining. The existing time series classification methods have the problems of low classification accuracy and low efficiency. To solve these problems, this paper proposes a T-CNN time series classification method based on a Gram matrix. Specifically, we perform wavelet threshold denoising on time series to filter normal curve noise, and propose a lossless transformation method based on the Gram matrix, which converts the time series to the time domain image and retains all the information of events. Then, we propose an improved CNN time series classification method, which introduces the Toeplitz convolution kernel matrix into convolution layer calculation. Finally, we introduce a Triplet network to calculate the similarity between similar events and different classes of events, and optimize the squared loss function of CNN. The proposed T-CNN model can accelerate the convergence rate of gradient descent and improve classification accuracy. Experimental results show that, compared with the existing methods, our T-CNN time series classification method has great advantages in efficiency and accuracy.

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“…Each time series segment is mapped to one of the three directions: convex, concave and linear. SAX-CP [21] captures the trend through the assessment of variation between a point and a segment mean. Some methods improve the SAX by increasing the number of extracted features.…”

Section: Introductionmentioning

confidence: 99%

An Improved Time Series Symbolic Representation Based on Multiple Features and Vector Frequency Difference

Yan¹,

Wu²,

Xiao³

2022

JCC

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Symbolic Aggregate approXimation (SAX) is an efficient symbolic representation method that has been widely used in time series data mining. Its major limitation is that it relies exclusively on the mean values of segmented time series to derive the symbols. So, many important features of time series are not considered, such as extreme value, trend, fluctuation and so on. To solve this issue, we propose in this paper an improved Symbolic Aggregate approXimation based on multiple features and Vector Frequency Difference (SAX_V-FD). SAX_VFD discriminates between time series by adopting an adaptive feature selection method. Furthermore, SAX_VFD is endowed with a new distance that takes into account the vector frequency difference between the symbolic sequence. We demonstrate the utility of the SAX_VFD on the time series classification task. The experimental results show that the proposed method has a better performance in terms of accuracy and dimensionality reduction compared to the so far published SAX based reduction techniques.

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A novel trend based SAX reduction technique for time series

Cited by 30 publications

References 10 publications

Predictive Maintenance on the Machining Process and Machine Tool

Predictive Maintenance on the Machining Process and Machine Tool

A T-CNN time series classification method based on Gram matrix

An Improved Time Series Symbolic Representation Based on Multiple Features and Vector Frequency Difference

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