A comparison of multivariate time series clustering methods. Meanwhile, the lack of adaptive regulation .

A comparison of multivariate time series clustering methods Current methods typically rely on space projection or representation learning for clustering but tend to overlook the significance and contribution of MTS dimensions, leading to a failure in accurately modeling the intricate correlations and dependencies among dimensions. Compared to classification, clustering is an Beyond clustering, we demonstrate the effectiveness of k-Shape to reduce the search space of one-nearest-neighbor classifiers for time series. Multivariate Time Series (MTS) have regained the focus of the research commu-nity with the effervescence of Big Data, Internet of Things and Cyber-Physical Systems. In this paper, we bridge this gap by proposing an efficient Feb 5, 2013 · While there are widely used methods for comparing univariate time series, most dynamical systems are characterized by multivariate time series. The main goal of the proposed algorithm is first to group windows of time series values with similar patterns by applying a clustering process. Also, I include below some interesting reading material for calculating similarity among multivariate time-series (the latest 2 are quite old but I think they are very interesting): Mar 1, 2022 · In this work, a new algorithm is proposed to predict both univariate and multivariate time series based on a combination of clustering, classification and forecasting techniques. Due to the high dimensionality of multivariate time series and most of the previous work concentrating on univariate time series clustering, a novel method which is Abstract Repeated patterns often appear in time series data. Table 3. In: Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, León, Spain, 4–6 September 2019, Proceedings, pp. However, in practical contexts, it would be beneficial to exploit some amount of available knowledge: even when this quantity is too Nov 1, 2005 · Time series clustering has been shown effective in providing useful information in various domains. Nov 25, 2020 · Aiming at the characteristics of multivariate time series, such as high dimensionality, the strong correlation of variables, and unequal length of sequences, this paper proposes the MSN-WDTW similarity measure for multivariate time series based on multi-dimensional segmentation norm representation and weighted dynamic time warping. Hence, I used this model for subsequent analysis. Abstract. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. This paper provides a new method for semi-supervised time point clustering based Sep 30, 2025 · Time series clustering is an unsupervised learning technique that groups data sequences collected over time based on their similarities. Many of the research efforts in this context have focused on proposing novel similarity measures for the underlying data. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. , in science and technology, medicine and pharmacy. Apr 28, 2025 · Multivariate time series (MTS) clustering has become a critical research area. Meanwhile, the lack of adaptive regulation Mar 20, 2018 · Aleksandr Blekh's answer in provides a lot of interesting reading material for time-series clustering methods and examples. The challenges in MTS clustering includes not only the selection of the algorithm but also the MTS representation and the similarity measurement among the instances. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. Dec 29, 2024 · Time-series clustering, an established unsupervised learning strategy that groups similar time series together, helps unveil hidden patterns in these complex datasets. To provide a Dec 29, 2024 · In this survey, we trace the evolution of time-series clustering methods from classical approaches to recent advances in neural networks. The method first utilizes Residual, TCN, and CNN-TCN to construct multi-view clustering remains largely untested. Similar to other types of data, annotations can be challenging to acquire, thus preventing from training time series classification models. Weassume while the other isbased onthe Mahalanobis distance between the that the Sep 15, 2020 · This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive — the state of the art repository of time series data. Big Data and the IoT explosion has made clustering Multivariate Time Series (MTS) one of the most effervescent research fields. Datasets arising fro… Oct 16, 2019 · In the past two decades, interest inGórecki, Tomasz the area of time series has soared and manyPiasecki, Paweł distance measures for time series have been proposed. The vast proliferation of time-series data across a wide range of fields has increased the relevance of evaluating the efectiveness and eficiency of these distance measures. Compared to classification, clustering is an unsupervised task and thus more applicable for analyzing massive time series without labels. Apr 5, 2025 · Finally, to evaluate the proposed method’s performance, we conducted many experiments on four real benchmark datasets. , DDC: Oct 1, 2015 · This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time-series approaches during the last decade and enlighten new paths for future works. Jul 1, 2021 · The detailed algorithm and the simulation experiments of the proposed BCNC method are reported. Mar 14, 2025 · Recently, there are several works based on the graph community detection idea to cluster multivariate time series. Mar 29, 2025 · The key contributions of this work include a systematic comparison of K-means, DBSCAN, and Spectral Clustering on three distinct high-dimensional datasets, analysis of the impact of dimensionality reduction techniques (PCA, t-SNE, and UMAP) on clustering performance, evaluation using multiple complementary metrics that provide a holistic assessment of clustering quality, and practical insights Mar 14, 2025 · This paper outlines the process of graph-based multivariate time clustering as four phases (referred to as framework), namely representation learning, similarity computing, relation network construction, and clustering, lists typical methods in each phase, and makes a comparison study of combinations of each phase methods (called strategies in Apr 12, 2023 · Alternative time series clustering algorithms using dynamic time warping (DTW) may overcome these limitations. Most existing approaches are single-view methods without considering the benefits of mutual-support multiple views Mar 14, 2025 · Download Citation | A comparison study of several strategies in multivariate time series clustering based on graph community detection | Time series data analysis, especially forecasting similarity between multivariate time-series datasets using two simi-larity factors. This paper introduces a new approach to multiscale and mul-tivariate time series clustering based on the method. Discovering these patterns is challeng-ing because time series need to be segmented and clustered simultaneously. In this paper, we have proposed a new method for time series clustering and compared the results to a collection of benchmarked time series clustering data. Compared with UTS, multivariate time series (MTS) consists of multiple components. Therefore, this chapter will devote a section to the Abstract The formation and analysis of clusters in multivariate time series can reveal interest-ing patterns and complex correlations in temporal data. First, a hierarchical detection algorithm is proposed to Jul 23, 2025 · ABSTRACT Clustering of multivariate time series using correlation-based methods reveals regime changes in relationships between variables across health, finance, and in-dustrial applications. The basics of Multivariate Time Series Clustering is one of the exploratory methods that can enable one to discover the different types of behavior that is manifested in different working periods of a system. Jul 28, 2021 · Time Series Clustering Algorithms Source: author I tested out many time series clustering algorithms on the sequential dataset. Identifying subphenotypes of infected patients is essential for personalized management. It is X-MeansTS common that the notion of multivariate time series clustering is defined as the grouping of a set of time series, and not the grouping of instances described by a set of common time series. Compared to classification, clustering is an Feb 18, 2021 · Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. DTW is an algorithm that computes the distance between temporal sequences by warping the sequences to an optimal alignment. Jan 7, 2023 · A deep learning based unsupervised clustering method for multivariate time series has been recently proposed in [16], which exploits a recurrent autoencoder integrating attention and gating mechanisms in order to produce effective embeddings of the input data. In recent years, deep learning has demonstrated outstanding performance in MTSF tasks. However, validating whether discovered clusters represent distinct relationships rather than arbitrary groupings remains a fundamental chal-lenge. Hence, this case, which we refer to as Case I, is a scenario where the two signals have the same dynamics. Aug 6, 2025 · Clustering multivariate time-series data is crucial for uncovering complex temporal patterns in dynamic environments, such as building indoor conditions and behavior where variables like temperature, humidity, and CO2 concentration evolve simultaneously. Time series clustering has been investigated In [25], five clustering methods were studied: k-means, multivariate Gaussian mixture, hierarchical clustering, spectral and nearest neighbor methods. Modern management systems increas-ingly rely on analyzing this data, highlighting the importance of eᇡ䷫cient processing techniques. In MTSF, modeling the correlations among different channels is critical, as leveraging information from other related channels can significantly improve the prediction In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. - bjdhafssa/MTS-Clustering-and-Averaging A hierarchical clustering algorithm is then employed to compare several time series given the quasi-distance matrix. Multivariate time-series (MTS) clustering is a fundamental technique in data mining with a wide range of real-world applications. From Bio-informatics to Business and Management, MTS are becoming more and more interesting as they allow to match events the co-occur in time but that is hardly noticeable. Abstract Time series data analysis, especially forecasting, classification, imputation, and anomaly detection, has gained a lot of research attention in recent years due to its prevalence and wide application. Dec 2, 2021 · We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering Some of his most significant contributions include extensive research on the effects of aggregation, methods of measuring information loss due to aggregation, new stochastic procedures of performing data disaggregation, model-free outlier detection techniques, robust methods of estimating autocor-relations, statistics for analyzing multivariate Abstract Distance measures are core building blocks in time-series analysis and the subject of active re-search for decades. State-of-the-art Machine Learning (ML) approaches for TS analysis and forecasting are becoming prevalent. , querying, indexing, classification, clustering, anomaly detection, and similarity search. Unfortunately, the most detailed experimental study in this area is outdated (over a decade old) and, naturally, does not reflect recent progress. Abstract The formation and analysis of clusters in multivariate time series can reveal interest-ing patterns and complex correlations in temporal data. Within a fair comparison framework, we (i) identify the top-performing method in each class; (ii) highlight previously over 4 days ago · Repeated patterns often appear in time series data. Long short-term memory and autoencoders are the most commonly used methods for detecting abnormal time points and time intervals. This review paper, provides a comprehensive overview of methodologies and advancements in multivariate time series forecasting, focusing on deep learning architectures, ensemble methods, and modeling techniques. First, they do not consider both the temporal features of each component A considerable amount of clustering algorithms take instance-feature matrices as their inputs. Given multivariate query sequence Q, to search the multivariate k NN sequences, each univariate time series is searched separately. Clustering methods have been frequently used to partition a domain of interest into distinct climatic zones. Mar 1, 2024 · The proposed method was validated using both visual and time series datasets, demonstrating its particular effectiveness in multivariate time series classification. Apr 19, 2025 · Aiming at the problems of existing time series data clustering methods, such as the lack of similarity metric universality, the influence of dimensional catastrophe, and the limitation of feature expression ability, a time series data clustering method based on unsupervised contrasting learning (UCL-TSC) is proposed. Jan 1, 2021 · Request PDF | A Comparison of Multivariate Time Series Clustering Methods | Big Data and the IoT explosion has made clustering Multivariate Time Series (MTS) one of the most effervescent research Nevertheless, when more than one Time Series (TS) is involved the clustering problem becomes much more challenging. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. The search results guide you to high Aug 1, 2023 · PDF | On Aug 1, 2023, Felipe Tomazelli Lima and others published A Large Comparison of Normalization Methods on Time Series | Find, read and cite all the research you need on ResearchGate Mar 14, 2025 · AbstractTime series data analysis, especially forecasting, classification, imputation, and anomaly detection, has gained a lot of research attention in recent years due to its prevalence and wide application. Overall, SBD, k-Shape, and k-MS emerge as domain-independent, highly accurate, and efficient methods for time-series comparison and clustering with broad applications. Nov 1, 2022 · This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Thus, principal component analysis Jun 1, 2017 · Beyond clustering, we demonstrate the effectiveness of k -Shape to reduce the search space of one-nearest-neighbor classifiers for time series. This paper provides a new method for semi-supervised time point clustering based Jun 28, 2021 · Again the affinity propagation algorithm is applied to clustering the synthetical correlation matrix, which realizes the clustering analysis of the original multivariate time series data. However, it is often expected a multivariate comparison method to consider the correlation between the variables as this correlation carries the real information in many cases. Most traditional MTS clustering methods may have two limitations. Most existing approaches … Dec 1, 2012 · In recent years, dynamic time warping (DTW) has begun to become the most widely used technique for comparison of time series data where extensive a priori knowledge is not available. 15 Clustering algorithms are then applied to the DTW distances to identify trajectory subphenotypes. Yet, comparison of multivariate time series has been limited to cases where they share a common dimensionality. In this paper, we consider the multivariate time series generated by vector autoregressive (VAR Mar 1, 2023 · In this study, we classify the anomalies into three types, namely abnormal time points, time intervals, and time series, and review the state-of-the-art deep learning techniques for the detection of each of these types. Clustering Multivariate Time Series: AI Techniques and Applications | SERP AIhome / posts / clustering multivariate time series Jun 28, 2021 · In view of the importance of various components and asynchronous shapes of multivariate time series, a clustering method based on dynamic time warping and affinity propagation is proposed. This study proposes an ensemble of MTS clustering methods that merges Severe infection can lead to organ dysfunction and sepsis. An MTS dataset is mapped to a multi-relationship network (MRN), which Jun 1, 2024 · The improvement of one-class classifiers’ performance through clustering of multivariate time series is considered in this paper. However, such works focus only on specific methods in each step, and a performance comparison of combinations of methods in different steps is lacking. Mar 14, 2025 · AbstractTime series data analysis, especially forecasting, classification, imputation, and anomaly detection, has gained a lot of research attention in recent years due to its prevalence and wide application. Additionally, it is possible to choose between unsupervised and semi-supervised methods to perform the clustering. Distance measures have been recognized as one of the fundamental building blocks in time-series analysis tasks, e. INTRODUCTION1 In the last few years, multivariate time series (MTS) data have been appeared extensively in scientific domains [1, 2] that represent valuable information subject to analysis, clustering, classification, indexing, and interpretation [3-5]. In this paper, we compare four clustering methods retrieved from the literature Abstract. In this paper, we consider the multivariate time series generated by vector autoregressive (VAR) models. From the two perspectives of the global and local properties information of multivariate time series, the relationship between the data objects is described. It is common that the notion of multivariate time series clustering is defined as the grouping of a set of time series, and not the grouping of Article "A Comparison of Multivariate Time Series Clustering Methods" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as "JST"). Until now, research efforts mainly focused on univariate time series, leaving multivariate cases largely under-explored. Unlike the traditional methods, the proposed method considers the relationship among variables and the distribution of the original data values of multivariate time series. Jun 28, 2021 · In view of the importance of various components and asynchronous shapes of multivariate time series, a clustering method based on dynamic time warping and affinity propagation is proposed. Importantly, this study (i) omitted multiple distance measures, including a classic measure in the time-series litera-ture Mar 27, 2025 · Abstract The explosion of Time Series (TS) data, driven by advancements in technology, ne-cessitates sophisticated analytical methods. Our analysis is based on an extensive experimental comparison on classification problems involving 10 normalization methods, 3 state-of-the-art classifiers, and 38 benchmark datasets. 15 Clustering algorithms are then applied to the DTW distances to identify trajectory subphenotypes. g. This study conducts a comparison of the performance of six clustering techniques: KMeans with Euclidean and Dynamic Time-Warping (DTW Abstract. Therefore, the concept of similarity is a very important one for time-series data clustering. Feb 15, 2025 · Abstract Multivariate Time Series Forecasting (MTSF) plays a crucial role across diverse fields, ranging from economic, energy, to traffic. However, previous techniques have neglected the time series (autocorrelation) component and have also handled seasonal features in a suboptimal way. 2 shows empirically aggregated proportions of runs where the λ and ¯∆ reject the null hypothesis of signal equality at level 5%. Real-world applications include daily fluctuations of the stock market (financial data analysis[6]), electrocardiogram data mining (medical Export, share and cite export More details on this result New search for: Further information on Dewey Decimal Classification , Production engineering. Although interest in MTS clustering is increasing, its performance is far from satisfactory. One latest way is based on the idea of graph Method Comparison - Clustering and Classification of Multivariate Stochastic Time Series in theidentity matrix. These challenges can be addressed in Grassmann manifold learning combined with state-space dynamical modeling, which allows existing Multivariate time series (MTS) data are ubiquitous in science and daily life, and how to measure their similarity is a core part of MTS analyzing process. This paper fills the gap by proposing a Our comparison of the proposed clustering algorithm to an existing online clustering algorithm for time-series and two online clustering algorithms for general data showed that our algorithm performs significantly better on two synthetic and one real-life datasets. c The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 ́A. Within a fair comparison framework, we (i) identify the top-performing method in each class; (ii) highlight previously over Sep 26, 2022 · This paper introduces a new approach to multiscale and multivariate time series clustering based on the X-MeansTS method. To date, though some approaches have been developed, they suffer from various drawbacks, such as high computational cost or loss of information. Nov 28, 2023 · To address this gap, we evaluate the impact of different normalization methods on time series data. Onesimilarity factor based s onprincipal omponent Inthis paper, anew clustering methodology forprocess data, analysis and the angles b tween theprincipal omponent subspaces particularly multivariate time-series data, ispresented. In this paper, we compare four clustering methods retrieved from the literature Recently, there are several works based on the graph community detection idea to cluster multivariate time series. The problem of pairwise similarity of time series is based on the underlying distance A comprehensive survey on community detection methods and applications in complex information networks Social Network Analysis and Mining, 2024 TS-TWC: A time series representation learning framework based on Time-Wavelet contrasting Biomedical Signal Processing and Control, 2023 Time series clustering based on relationship network and community detection Expert Systems with Applications, 2022 Mar 28, 2023 · 4 clusters, for each of the 15 methods. To address these gaps, we eval-uate 84 time-series clustering methods across 10 method classes from data mining, machine learning, and deep learning. Four proximity measures were used in the experiments: Pearson and Spearman correlation coefficient, cosine similarity and the euclidean distance. Oct 7, 2021 · Technologies such as Big Data and IoT have shown the need for intelligent unsupervised processing of Multivariate Time Series (MTS), MTS clustering among them. A proof of concept in multivariate time series clustering using recurrent neural networks and SP-lines. In this survey, we trace the evolution of time-series clustering methods from classical approaches to recent advances in neural networks. Dec 24, 2024 · Multivariate time series (MTS) clustering has been an essential research topic in various domains over the past decades. In this context, clustering methods can be an appropriate alternative as they create homogeneous groups allowing a better analysis of the data structure. For use in situations where components of the multivariate time series are measured in different units of scale, a modified quasi-distance based on a profile likelihood based estimation of the scale parameter is described. It is unknown how different time series clustering algorithms compare in identifying these Jul 1, 2021 · Our study led us to develop a novel method based on complex networks for multivariate time series clustering (BCNC). Specifically, the benchmark examines eight popular clustering methods representing three categories of clustering algorithms (partitional, hierarchical and density clustering remains largely untested. It provides free access to secondary information on researchers, articles, patents, etc. A simulation study is done to compare the distance measures for This Git repository focuses on comparing averaging methods and clustering techniques for multivariate time series data. While this approach works well for univariate Jun 18, 2025 · Distance measures are fundamental to time series analysis and have been extensively studied for decades. 346–357 (2019) Jul 14, 2024 · Most time series clustering methods mainly focus on univariate time series (UTS). As both proportions are close to 5% Feb 5, 2013 · While there are widely used methods for comparing univariate time series, most dynamical systems are characterized by multivariate time series. Nevertheless, when more than one Time Series (TS) is involved the clustering problem becomes much more challenging. This experiment was repeated 200 times and the boxplots Figure 2: Comparison of similarity index of clustering methods of multivariate time series con-sisting of line Multivariate time-series (MTS) clustering is a fundamental technique in data mining with a wide range of real-world applications. Upon closer analysis, time series k-means with the dynamic time warping metric produced the most accurate results. Aug 29, 2020 · In this paper, we compare four clustering methods retrieved from the literature analyzing their performance on five publicly available data sets. Our empirical results demonstrate that our proposed clustering approach is able to cluster time series without using the actual data point values in the clustering algorithm. Traditional approaches such as Mar 1, 2021 · In this paper, we propose a clustering-based approach to detect anomalies concerning the amplitude and the shape of multivariate time series. Time-series clustering, an established unsupervised learning strategy that groups similar time series together, helps unveil hidden patterns in these complex datasets. Alterna-tive time series clustering algorithms using dynamic time warping (DTW) may overcome these limitations. BCNC includes a new method for mapping multivariate time series into complex networks and a new method to visualize multivariate time series. Because traditional clustering methods rarely consider the component correlations of a multivariate time series (MTS), an MTS clustering method based on a component relationship network (CRN) is proposed in the present study. Unlike traditional clustering, it accounts for temporal dependencies, shifts in trend and variable sequence lengths. However, such works focus only on specific method in each step, and a performance comparison of combinations of methods in different steps is lack. The experimental results on various datasets show that BCNC is superior to traditional multivariate time series clustering methods. Furthermore, the existing Apr 16, 2012 · Due to the high dimensionality of multivariate time series and most of the previous work concentrating on univariate time series clustering, a novel method which is based on common principal component analysis, is proposed to achieve multivariate time series clustering more fast and accurately. . This paper briefly Jun 27, 2024 · Multivariate time series forecasting is a critical task with applications across various domains, including finance, energy demand, and climate modeling. However, with the countless techniques to estimate similarity between MTS, this field suffers from a lack of Jul 15, 2019 · Time series clustering is often applied to pattern recognition and also as the basis of the tasks in the field of time series data mining including dimensionality reduction, feature extraction, classification and visualization. The determination of clusters of time series is extremely challenging because of the difficulty in defining similarity across different time series which may be scaled and translated differently both on the temporal and behavioral dimensions. This is a great pity since many of these algorithms are effective, robust, efficient, and easy to use. Oct 16, 2021 · Time series are ubiquitous in data mining applications. First, we use a sliding window to generate a set of multivariate subsequences and thereafter apply an extended fuzzy clustering to reveal a structure present within the generated multivariate subsequences. However, inherent properties of MTS data—namely, temporal dynamics and inter-variable correlations—make MTS clustering challenging. 1. Our anal-ysis spans 128 time-series datasets and uses rigorous statistical methods. Discovering these patterns is challenging because time series need to be segmented and clustered simultaneously. However, traditional clustering methods based on distance metrics fall short to discover interpretable characteristics and structures reflected by these clusters. These methods make use of different TS representation and distance measurement functions. Overall, SBD, k -Shape, and k -MS emerge as domain-independent, highly accurate, and efficient methods for time-series comparison and clustering with broad applications. The multivariate data sequences S with n dimensions are divided into n univariate time series, and each dimension is a univariate time series. By Jul 1, 2021 · Clustering is a powerful technique for providing class labels of data objects for learning guidance. Sep 23, 2023 · Traditional time series models, like autoregressive integrated moving average (ARIMA), rely on past values of the target variable to make predictions. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. Clusterwise regression is a useful approach that enables the segmentation and clustering of the time series simultaneously. Big Data and the IoT explosion has made clustering Multi-variate Time Series (MTS) one of the most e ervescent research elds. The experimental results show that the performance of this method is better than that of several baseline methods, and it is suitable for multivariate time series data with multivariate solid correlation. gidg yfovjd bxqyn mhgqu rkb slpvkr kffupx gbwpg zvlxf pbmyjy qmrvdr mitois rhtt reyqgj djkfp