Tag Archives: Clustering Of Time Series

Change point detection through self-supervised learning

June 7, 2024 10:53 ,

Xiangyu Bao, Liang Chen, Jingshu Zhong, Dianliang Wu, Yu Zheng, A self-supervised contrastive change point detection method for industrial time series, Engineering Applications of Artificial Intelligence, Volume 133, Part B, 2024, DOI: 10.1016/j.engappai.2024.108217.

Manufacturing process monitoring is crucial to ensure production quality. This paper formulates the detection problem of abnormal changes in the manufacturing process as the change point detection (CPD) problem for the industrial temporal data. The premise of known data property and sufficient data annotations in existing CPD methods limits their application in the complex manufacturing process. Therefore, a self-supervised and non-parametric CPD method based on temporal trend-seasonal feature decomposition and contrastive learning (CoCPD) is proposed. CoCPD aims to solve CPD problem in an online manner. By bringing the representations of time series segments with similar properties in the feature space closer, our model can sensitively distinguish the change points that do not conform to either historical data distribution or temporal continuity. The proposed CoCPD is validated by a real-world body-in-white production case and compared with 10 state-of-the-art CPD methods. Overall, CoCPD achieves promising results by Precision 70.6%, Recall 68.8%, and the mean absolute error (MAE) 8.27. With the ability to rival the best offline baselines, CoCPD outperforms online baseline methods with improvements in Precision, Recall and MAE by 14.90%, 11.93% and 43.93%, respectively. Experiment results demonstrate that CoCPD can detect abnormal changes timely and accurately.

See also: https://doi.org/10.1016/j.engappai.2024.108155

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A brief summary of the state of the art in time series clustering

November 17, 2023 09:22 ,

Hailin Li, Zechen Liu, Xiaoji Wan, Time series clustering based on complex network with synchronous matching states, Expert Systems with Applications, Volume 211, 2023 DOI: 10.1016/j.eswa.2022.118543.

Due to the extensive existence of time series in various fields, more and more research on time series data mining, especially time series clustering, has been done in recent years. Clustering technology can extract valuable information and potential patterns from time series data. This paper proposes a time series Clustering method based on Synchronous matching of Complex networks (CSC). This method uses density peak clustering algorithm to identify the state of each time point and obtains the state sequence according to the timeline of the original time series. State sequences is a new method to represent time series. By comparing two state sequences synchronously, the length of state sequence with step is calculated and the similarity is presented, which forms a new method to calculate the similarity of time series. Based on the obtained time series similarity, the relationship network of time series is constructed. Simultaneously, the community discovery technology is applied to cluster the relationship network and further achieve the complete time series clustering. The detailed process and simulation experiments of CSC method are given. Experimental results on different datasets show that CSC method is superior to other traditional time series clustering methods.

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Non-parameterical detection of regimes in time series data (SODA), and its use in fuzzy forecasting

November 10, 2023 09:17 ,

Shivani Pant, Sanjay Kumar, IFS and SODA based computational method for fuzzy time series forecasting, Expert Systems with Applications, Volume 209, 2022 DOI: 10.1016/j.eswa.2022.118213.

Time series forecasting has fascinated a great deal of interest from various research communities due to its wide applications in medicine, economics, finance, engineering and many other crucial fields. Various studies in past have shown that intuitionistic fuzzy sets (IFSs) not only handle non-stochastic non-determinism in time series forecasting but also enhance accuracy in forecasted outputs. Clustering is another one of the methods that improves accuracy of time series forecasting. The contribution of this research work is a novel computational fuzzy time series (FTS) forecasting method which relies on IFSs and self-organized direction aware (SODA) approach of clustering. The usage of SODA aids in making the proposed FTS forecasting method as autonomous as feasible, as it does not require human intervention or prior knowledge of the data. Forecasted outputs in proposed FTS forecasting method are computed using a weighted formula and weights are optimized using grey wolf optimization (GWO) method. Proposed FTS is applied to forecast enrolments of the University of Alabama and market price of State Bank of India (SBI) share at Bombay stock exchange (BSE), India and performance is compared in terms of root mean square error (RMSE), average forecasting error (AFE) and mean absolute deviation (MAD). Goodness of the proposed FTS forecasting method in forecasting enrolments of the University of Alabama and market price of SBI share is also tested using coefficient of correlation and determination, criteria of Akaike and Bayesian information.

See also https://babel.isa.uma.es/kipr/?p=1550

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Semi-Markov HMMs for modelling time series in milling machines

October 27, 2023 05:32 ,

Kai Li, Chaochao Qiu, Xinzhao Zhou, Mingsong Chen, Yongcheng Lin, Xianshi Jia, Bin Li, Modeling and tagging of time sequence signals in the milling process based on an improved hidden semi-Markov model, Expert Systems with Applications, Volume 205, 2022 DOI: 10.1016/j.eswa.2022.117758.

Vibration signals are widely used in the field of tool wear, tool residual life prediction and health monitoring of mechanical equipment. However, the current data-driven research methods mostly rely on high-value and high-density labeled data to establish relevant models and algorithms. Therefore, it is of great significance to solve the problem of automatic tagging of data, realize automatic signal interception, and enhance the value density of manufacturing process data. The Hidden semi-Markov model (HSMM) can describe the real spatial statistical characteristics of random models through observable data. As HSMM does not need the real labels of the signal, it can reduce tagging work to improve the marking efficiency. In this paper, an improved HSMM was proposed to model and tag the spindle vibration signals in the milling process. First, the Mel frequency cepstral coefficients (MFCCs) were extracted as observation sequences from the collected spindle vibration signals, and the dimension of the original features was reduced by linear discriminant analysis (LDA). Subsequently, a signal automatic tagging model based on HSMM was developed, in which the state duration can be explicitly modeled. Finally, the evaluation of the proposed methodology was carried out in the laboratory and real industry machining. The experimental results confirmed the effectiveness and robustness of the proposed model.

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Clustering time series through the moments of the corresponding regimes using fuzzy

October 13, 2023 09:02 ,

Roy Cerqueti, Pierpaolo D\u2019Urso, Livia De Giovanni, Massimiliano Giacalone, Raffaele Mattera, Weighted score-driven fuzzy clustering of time series with a financial application, Expert Systems with Applications, Volume 198, 2022 DOI: 10.1016/j.eswa.2022.116752.

Time series data are commonly clustered based on their distributional characteristics. The moments play a central role among such characteristics because of their relevant informative content. This paper aims to develop a novel approach that faces still open issues in moment-based clustering. First of all, we deal with a very general framework of time-varying moments rather than static quantities. Second, we include in the clustering model high-order moments. Third, we avoid implicit equal weighting of the considered moments by developing a clustering procedure that objectively computes the optimal weight for each moment. As a result, following a fuzzy approach, two weighted clustering models based on both unconditional and conditional moments are proposed. Since the Dynamic Conditional Score model is used to estimate both conditional and unconditional moments, the resulting framework is called weighted score-driven clustering. We apply the proposed method to financial time series as an empirical experiment.

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