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Time-series clustering – A decade review
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“Methods for clustering time-series data are commonly divided into three classes (Aghabozorgi et al., 2015): modelbased, feature-based and shape-based approaches.”
From Ink and algorithm: Exploring temporal dynamics in generative AI ‐assisted writing (2026) · cites this paper as Aghabozorgi et al., 2015
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Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Dynamic Programming Algorithm Optimization for Spoken Word Recognition
Sakoe, H., & Chiba, S. (1978) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1978.1163055 · Cited by 6,727
A massively parallel architecture for a self-organizing neural pattern recognition machine
Carpenter, G. A., & Grossberg, S. (1987) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(87)80014-2 · Cited by 2,525
BIRCH: an efficient data clustering method for very large databases
Zhang, T., Ramakrishnan, R., & Livny, M. (1996) · ACM SIGMOD Record · doi:10.1145/235968.233324 · Cited by 2,097
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Items 401–500 of 1,000
Bottaz-Bosson, G., Hamon, A., Pépin, J., Bailly, S., & Samson, A. L. (2021) · Statistics in Medicine · doi:10.1002/sim.9130 · Cited by 17
Secor, D. H., O’Brien, M. H. P., Gahagan, B. I., Fox, D. A., Higgs, A. L., & Best, J. E. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242797 · Cited by 23
“equivalent, and does not have the requirement of equal-length time series [48].”
BackgroundDLCSS: A new similarity measure for time series data mining
Soleimani, G., & Abessi, M. (2020) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2020.103664 · Cited by 17
Constrained Distance-Based Clustering for Satellite Image Time-Series
Lampert, T. A., Lafabrègue, B., Dao, T.-B.-H., Serrette, N., Vrain, C., & Gançarski, P. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2950406 · Cited by 14
“A number of works give an in-depth background on clustering time-series [30], [35]–[40].”
BackgroundLong-term Prediction of Motion Trajectories Using Path Homology Clusters
Carvalho, J., Vejdemo-Johansson, M., Pokorny, F. T., & Kragic, D. (2019) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros40897.2019.8968125 · Cited by 17
“Generally, this is achieved through distance-based clustering methods, and a recent survey of such distance based methods can be found in [17].”
BackgroundMoayedi, A., Abbaspour, R., & Chehreghan, A. (2019) · Ann. GIS · doi:10.1080/19475683.2019.1679254 · Cited by 17
“Generally, spatial trajectories are represented by a sequence of timestamped locations as follows (Aghabozorgi, Shirkhorshidi, and Wah 2015): where id k is the location identi fi er, loc k is the position at the time t k , and A k is other descriptive data related to the point.”
BackgroundLinear Time Complexity Time Series Clustering with Symbolic Pattern Forest
Li, X., Lin, J., & Zhao, L. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/406 · Cited by 17
“Time series clustering has been applied in a variety of domains including astronomy [Rebbapragada et al., 2009], finance [Kumar et al., 2002], and so on [Aghabozorgi et al., 2015].”
MethodMolina-García, Á., Fernández-Guillamón, A., Gómez-Lázaro, E., Honrubia-Escribano, A., & Bueso, M. (2019) · IEEE Access · doi:10.1109/access.2019.2902242 · Cited by 17
“A large number of functional clustering applications can been found in the literature review [49], mainly focused on life sciences [50].”
BackgroundPlato: Approximate Analytics over Compressed Time Series with Tight Deterministic Error GuaranteesPreprint
Boursier, E., Brito, J., Lin, C., & Papakonstantinou, Y. (2018) · arXiv.org · doi:10.48550/arxiv.1808.04876 · Cited by 17
“Following the prior work [2, 30], Plato selects the candidate estimation function that minimizes the Euclidean distance between the original values and the estimated values produced by the function to be the final estimation function.”
MethodA framework for delineating the regional boundaries of PM2.5 pollution: A case study of China
Liu, J., Li, W., & Wu, J. (2018) · Environmental Pollution · doi:10.1016/j.envpol.2017.12.064 · Cited by 20
Bardinas, J. P., Aldrich, C., & Napier, L. F. A. (2018) · Processes · doi:10.3390/pr6020017 · Cited by 17
“In this paper, the Euclidean distance metric is used, owing to its simple, yet competitive capability in dealing with time series classification [12].”
MethodLi, Z., Fan, H., Liao, Z.-Y., Wang, Y., & Wei, F.-W. (2025) · Science Advances · doi:10.1126/sciadv.adq3938 · Cited by 16
“DTW is a shape-based time-series clustering method that aligns two time series to find their optimal match ( 76 ).”
Chen, F., Li, S., Lin, L., & Huang, X. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12494-5 · Cited by 19
“The review by Aghabozorgi et al. (2015) summarized a variety of external and internal CVIs proposed in the literature.”
Predicting CKD progression using time-series clustering and light gradient boosting machines
Saito, H., Yoshimura, H., Tanaka, K., Kimura, H., Watanabe, K., Tsubokura, M., Ejiri, H., Zhao, T.-C., Ozaki, A., Kazama, S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52251-9 · Cited by 16
“Time-series clustering is one of the methods of cluster analysis utilizing time-series data, which is used in diverse scientific fields from gene expression data in biology to stock market analysis in finance 21 .”
MethodAccelerating k-Shape Time Series Clustering Algorithm Using GPU
Wang, X., Song, R., Xiao, J., Li, T., & Li, X.-Q. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3298148 · Cited by 16
“In addition, the euclidean distances used by traditional k-medoids and k-means in sequence similarity calculation also lead to them not capturing the sequence offset effectively on the time axis[46], [47], [48].”
Andreo, V., Porcasi, X., Guzman, C., Lopez, L., & Scavuzzo, C. (2021) · Insects · doi:10.3390/insects12100919 · Cited by 16
“Time Series Clustering We tested different partitional time series clustering algorithms and different distance measures with number of clusters (k) ranging from 3 to 10 (Table 1) [27].”
BackgroundMethodTime series clustering in linear time complexity
Li, X., Lin, J., & Zhao, L. (2021) · Data mining and knowledge discovery · doi:10.1007/s10618-021-00798-w · Cited by 16
REST: Reciprocal Framework for Spatiotemporal-coupled Predictions
Lin, H., Fan, Y., Zhang, J., & Bai, B. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449928 · Cited by 14
“, correlation or causation) among time series and cluster them as a graph [1].”
BackgroundStudy on Clustering of Free-Floating Bike-Sharing Parking Time Series in Beijing Subway Stations
Xu, D.-D., Bian, Y., Rong, J., Wang, J., & Yin, B.-C. (2019) · Sustainability · doi:10.3390/su11195439 · Cited by 16
“Meanwhile, it is also a strength in time series clustering, because it is usually hard to define the number of clusters in real-world problems [28].”
BackgroundMethodInfluentialIterative multiscale dynamic time warping (IMs-DTW): a tool for rainfall time series comparison
Dilmi, M., Barthès, L., Mallet, C., & Chazottes, A. (2019) · International Journal of Data Science and Analysis · doi:10.1007/s41060-019-00193-1 · Cited by 16
An MBO scheme for clustering and semi-supervised clustering of signed networks
Cucuringu, M., Pizzoferrato, A., & van Gennip, Y. (2019) · Communications in Mathematical Sciences · doi:10.4310/cms.2021.v19.n1.a4 · Cited by 16
“Finally, another main motivation for our proposed line of work arises in the area of time series analysis, where clustering time series data is a task of particular importance [21].”
BackgroundTight Hardness Results for Consensus Problems on Circular Strings and Time Series
Bulteau, L., Froese, V., & Niedermeier, R. (2018) · SIAM Journal on Discrete Mathematics · doi:10.1137/19m1255781 · Cited by 16
“, xk, c ′), where xj := (b, sj [1], b, sj [2], .”
BackgroundOptimal transport vs. Fisher-Rao distance between copulas for clustering multivariate time series
Marti, G., Andler, S., Nielsen, F., & Donnat, P. (2016) · Symposium on Software Performance · doi:10.1109/ssp.2016.7551770 · Cited by 16
“Which class of distances should one use when the data-points are time series? Several surveys and experimental studies [1, 2, 3] have tried to organize and benchmark this tentacular field of clustering time series: many application domains (e.”
BackgroundAutoencoder-Enhanced Clustering: A Dimensionality Reduction Approach to Financial Time Series
Cortés, D. G., Onieva, E., Pastor-López, I., Trinchera, L., & Wu, J. L. (2024) · IEEE Access · doi:10.1109/access.2024.3359413 · Cited by 23
“For example, a review by Aghabozorgi et al. [9] found that many authors focused on representing time series data in a lower dimension to be consistent with conventional clustering algorithms.”
Urban ozone trends in Europe and the USA (2000–2021)
Nelson, B. S., & Drysdale, W. S. (2025) · Atmospheric Chemistry and Physics · doi:10.5194/acp-25-16009-2025 · Cited by 15
García, Y. E., Chou-Chen, S. W., Barboza, L. A., Daza–Torres, M. L., Montesinos-López, J. C., Vásquez, P., Calvo, J. G., Nuno, M. A., & Sánchez, F. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0002417 · Cited by 13
“Wavelets have been used to study time series with different purposes: to evaluate the main characteristics of non-stationary time series [27, 16, 28, 15, 29], to analyze spatial patterns [16, 14], to study the relationship between population and environmental time series; finding the phase and/or synchrony patterns [1…”
MethodClustering of Longitudinal Data: A Tutorial on a Variety of Approaches
Teuling, N. D., Pauws, S. C., & van den Heuvel, E. R. (2026) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.70052 · Cited by 22
“, piecewise-constant approximation) (Aghabozorgi et al., 2015; Wang et al., 2013).”
InfluentialImpact of credit on sustainable agricultural development in India
Kumari, A., & Garg, V. (2021) · Journal of Sustainable Finance & Investment · doi:10.1080/20430795.2021.1964811 · Cited by 15
A Systematic Review of Packages for Time Series Analysis
Siebert, J., Groß, J., & Schroth, C. (2021) · International Conference on Time Series and Forecasting (ITISE) · doi:10.3390/engproc2021005022 · Cited by 12
“The literature contains many reviews, either focusing on analysis tasks and methods (see, for instance, these reviews on forecasting [17,24,28,40], clustering and classification [1,2,6,15,39], anomaly detection [5,9,46], changepoint analysis [4,33,44], pattern recognition [43,45], or dimensionality reduction [30]) or…”
BackgroundClustering framework based on multi-scale analysis of intraday financial time series
Shi, Y., Li, B., Du, G.-T., & Dai, W. (2021) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.125728 · Cited by 15
Incremental fuzzy clustering of time series
Wang, L., Xu, P., & Ma, Q. (2021) · Fuzzy Sets Syst. · doi:10.1016/j.fss.2021.01.002 · Cited by 15
Time Series Averaging Using Multi-Tasking Autoencoder
Debella, T. T., Devanne, M., Weber, J., Hailemariam, D., & Forestier, G. (2020) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai50040.2020.00163 · Cited by 16
“Like their non temporal counterparts, time series are also analyzed in a supervised, unsupervised and semi supervised manner [3]–[5].”
BackgroundCluster-based Aggregate Load Forecasting with Deep Neural Networks
Cini, A., Luković, S., & Alippi, C. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207503 · Cited by 16
“Clustering of time-series is a challenging research problem [13], with a wide range of applications from customer segmentation [14] and stock-market analysis [15] to biology [16].”
BackgroundFernández-Rodríguez, S., Manzano, J. M. M., Colín, A. M., Pecero-Casimiro, R., Buters, J., & Oteros, J. (2020) · Science of the Total Environment · doi:10.1016/j.scitotenv.2020.139363 · Cited by 15
Massahi, M., Mahootchi, M., & Arshadikhamseh, A. (2020) · Empirical Economics · doi:10.1007/s00181-019-01802-5 · Cited by 15
Benchmarking Deep Learning for Time Series: Challenges and Directions
Huang, X., Fox, G., Serebryakov, S., Mohan, A., Morkisz, P. M., & Dutta, D. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005496 · Cited by 14
“…I) While statistical and econometric models have been well studied for decades in classical tasks such like forecasting and anomaly detection [5], [6], DL based approaches have only recently demonstrated the high potential of success as they bring new solutions to complex time series problems…”
BackgroundXiao, L., Chen, X., Zhang, R., & Zhang, Z.-C. (2019) · Water · doi:10.3390/w11102104 · Cited by 15
“Among the clustering techniques, hierarchical clustering and partitioning clustering are two main approaches in time series clustering algorithms [17], popularly applied to group the similar sites into a homogeneous sub-region based on at-site SPI time series [16,18].”
BackgroundMethodSpatial controls on the distribution and dynamics of a marginal snowpack in the Australian Alps
Bilish, S. P., Callow, J., McGrath, G., & McGowan, H. (2019) · Hydrological Processes · doi:10.1002/hyp.13435 · Cited by 15
“Time series may be clustered based on similarity in time, shape, or change, with similarity in time a special case of similarity in shape (Aghabozorgi et al., 2015).”
BackgroundSpeed up dynamic time warpingof multivariate time series
Li, Z.-X., Zhang, F., Nie, F., Li, H., & Wang, J. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-181736 · Cited by 15
“But Euclidean distance is sensitive to scaling and shifting of sequences in the time axis, and cannot measure the similarity of the sequences of different lengths [18].”
Cluster-based energy consumption forecasting in smart grids
Shchetinin, E. Y. (2018) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/1205/1/012051 · Cited by 15
“Cluster analysis is one of the types of these methods [5, 6, 7].”
MethodGhalyan, N. F., Miller, D. J., & Ray, A. (2018) · Neural Computation · doi:10.1162/neco_a_01101 · Cited by 15
“By contrast, in time series clustering (Aghabozorgi, Shirkhorshidi, & Wah, 2015; Nguyen, McLachlan, Orban, Bellec, & Janke, 2017) one has a collection of, for example, M time series, with each of these time series assigned to one of | A | clusters.”
BackgroundProbabilistic Temporal Fusion Transformers for Large-Scale KPI Anomaly Detection
Luo, H., Zheng, Y., Kang, C., & Zhao, S. (2024) · IEEE Access · doi:10.1109/access.2024.3353201 · Cited by 21
Bi, C., Liu, D., Zhu, L., Li, S., Wu, X., & Lü, C. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125751 · Cited by 14
Zhang, Y., Zhang, W., Li, Y.-L., Wen, L., & Sun, X. (2024) · Expert systems with applications · doi:10.1016/j.eswa.2024.126135 · Cited by 14
Co-clustering: A Survey of the Main Methods, Recent Trends, and Open Problems
Battaglia, E., Peiretti, F., & Pensa, R. (2024) · ACM Computing Surveys · doi:10.1145/3698875 · Cited by 14
Unsupervised Anomaly Detection in Spatio‐Temporal Stream Network Sensor Data
Santos–Fernández, E., Hoef, J. M. V., Peterson, E. E., McGree, J. M., Villa, C. A., Leigh, C., Turner, R. D. R., Roberts, C., & Mengersen, K. L. (2024) · Water Resources Research · doi:10.1029/2023wr035707 · Cited by 23
“This approach allows clustering sites based on similarity or time series distance (Aghabozorgi et al., 2015).”
Jalal, M., & Elmaghraby, A. (2024) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer19030081 · Cited by 14
“However, density-based clustering is not widely employed for time series data clustering due to its relatively high complexity [34].”
BackgroundMethodInfluentialCórtez, J. C., López, J. C., Ullón, H. R., Giesbrecht, M., & Rider, M. J. (2024) · Journal of Control Automation and Electrical Systems · doi:10.1007/s40313-024-01099-5 · Cited by 16
“Several authors (Cuturi & Blondel, 2017; Li et al., 2022; Aghabozorgi et al., 2015) recommend the use of algorithms such as DTW and soft-DTW to determine the optimal distance metric in time series clustering.”
Observed Spatiotemporal Variability in the Annual Sea Level Cycle Along the Global Coast
Barroso, A., Wahl, T., Li, S., Enríquez, A., Morim, J., Dangendorf, S., Piecuch, C., & Thompson, P. (2024) · Journal of Geophysical Research - Oceans · doi:10.1029/2023jc020300 · Cited by 14
Naethe, P., Sanctis, A. D., Burkart, A., Campbell, P., Colombo, R., Mauro, B. D., Damm, A., El-Madany, T., Fava, F., Gamon, J., et al. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114013 · Cited by 14
Ribeiro, R., & Fanzeres, B. (2023) · Energy and AI · doi:10.1016/j.egyai.2023.100320 · Cited by 14
Hatherall, O., Niri, M. F., Barai, A., Li, Y., & Marco, J. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107091 · Cited by 14
An autocorrelation incremental fuzzy clustering framework based on dynamic conditional scoring model
Zhang, Y., Li, X., Wang, L., Fan, S., Zhu, L., & Jiang, S.-H. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119567 · Cited by 14
Zhang, Y., Li, X., Jiang, S.-H., Tseng, M., Wang, L., & Fan, S. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110395 · Cited by 14
Gauthier-Manuel, H., Bernard, N., Boilleaut, M., Giraudoux, P., Pujol, S., & Mauny, F. (2023) · Environment International · doi:10.1016/j.envint.2023.107859 · Cited by 15
Rodríguez, M., Díaz-Amor, G., Morales, J., Koketsu, Y., & Piñeiro, C. (2023) · Porcine Health Management · doi:10.1186/s40813-022-00300-y · Cited by 14
“[19], who combined the use of electronic feeders as well as the development of specific computational methods for storing and dealing with time series [20] to provide a different description of feed intake.”
BackgroundDual-meta pool method for wind farm power forecasting with small sample data
Liu, L., Wang, J.-J., Li, J., & Wei, L. (2022) · Energy · doi:10.1016/j.energy.2022.126504 · Cited by 14
Gharavi, A., Hassan, M., Gholinezhad, J., Ghoochaninejad, H., Barati, H., Buick, J. M., & Abbas, K. A. (2022) · Int. J. Intell. Networks · doi:10.1016/j.ijin.2022.08.001 · Cited by 14
Learning Embedded Representation of the Stock Correlation Matrix Using Graph Machine Learning
Sarmah, B., Nair, N., Jain, R., Mehta, D., & Pasquali, S. (2022) · IEEE Conference on Computational Intelligence for Financial Engineering & Economics · doi:10.1109/cifer62890.2024.10772849 · Cited by 14
“In this section, we conduct a comparison of V-measure values obtained from traditional time-series K -means clustering analysis[53], utilizing various distance metrics as hyperparam-eters, such as Euclidean, Dynamic Time Warping (DTW), and Soft Dynamic Time Warping (Soft-DTW) using the Tslearn…”
Sadiq, M. U., Kwak, K., Dayan, E., & Initiative, A. D. N. (2022) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00941-1 · Cited by 15
“The shape-based DTW method is particularly well-suited to dynamic time-series data with potential temporal drift, showing better accuracy than linear models [34, 36].”
MethodGraph-based Clustering for Time Series Data
Li, P., Boubrahimi, S. F., & Hamdi, S. M. (2021) · 2021 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata52589.2021.9671398 · Cited by 14
“…is stored under the form of time-series data, for example, sales data, stock prices, exchange rates in finance, weather data, biomedical measurements (e.g., blood pressure and electrocardiogram measurements), biometrics data (image data for facial recognition), particle tracking in physics, etc [2].”
BackgroundRobot Collision Detection Without External Sensors Based on Time-Series Analysis
Zhang, T., Ge, P., Zou, Y.-B., & He, Y.-W. (2021) · doi:10.1115/1.4048782 · Cited by 14
Massara, P., Keown-Stoneman, C. D. G., Erdman, L., Ohuma, E., Bourdon, C., Maguire, J., Comelli, E., Birken, C., & Bandsma, R. H. J. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyab021 · Cited by 14
Averaging Methods using Dynamic Time Warping for Time Series Classification
Datta, S., Karmakar, C., & Palaniswami, M. (2020) · IEEE Symposium Series on Computational Intelligence · doi:10.1109/ssci47803.2020.9308409 · Cited by 14
“Modern sensing technologies, increasing processing power and data storage capacities have led to a major increase in the generation of time series data [1].”
BackgroundTime Series Clustering: A Complex Network-Based Approach for Feature Selection in Multi-Sensor Data
Bonacina, F., Miele, E. S., & Corsini, A. (2020) · Modelling · doi:10.3390/modelling1010001 · Cited by 14
Random projections and sampling algorithms for clustering of high-dimensional polygonal curves
Meintrup, S., Munteanu, A., & Rohde, D. (2019) · Neural Information Processing Systems · doi:10.48550/arxiv.1907.06969 · Cited by 14
“While the analysis of time-series is a well-studied topic, cf. Hamilton (1994); Liao (2005); Aghabozorgi et al. (2015), there are only few approaches that take high-dimensional multivariate time-series into account.”
BackgroundQin, X., Yang, J., Li, P., Sun, W.-D., & Liu, W. (2019) · Remote Sensing · doi:10.3390/rs11111358 · Cited by 14
“According to whether there are true labels as references, the commonly used clustering evaluation indicators can be divided into internal indices and external indices [21].”
BackgroundMunthe, A. D. (2019) · Jurnal Litbang Sukowati : Media Penelitian dan Pengembangan · doi:10.32630/sukowati.v2i2.61 · Cited by 14
Multivariate dynamic time warping in automotive applications: A review
Moser, U., & Schramm, D. (2019) · Intelligent Data Analysis · doi:10.3233/ida-184130 · Cited by 14
“In the problem definition of time series clustering as defined in [1,17], a dataset X q = { X 1 , X 2 , . . . , X q } of q time series data is given.”
Making the dynamic time warping distance warping-invariant
Jain, B. J. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.05.016 · Cited by 14
“Consequently, a plethora of task-specific proximity measures have been devised [1, 3].”
BackgroundHerrera, M., Proselkov, Y., Pérez-Hernández, M., & Parlikad, A. (2021) · IEEE Access · doi:10.1109/access.2021.3050014 · Cited by 14
“Among the multiple possibilities that can be adapted the best to vector TS analysis, the popular ones are based on joint models of multiple TS [59], [60], TS clustering [61], symbolic representation [62], and matrix profile [63].”
BackgroundZhang, C., Xiao, X., Wang, Y., Hou, Z., Huang, S., Hu, W., Hu, M., & Huang, R. (2025) · Energy · doi:10.1016/j.energy.2025.137995 · Cited by 18
The sustainable energy development dilemma in European countries: a time-series cluster analysis
Dugo, V., Gálvez-Ruiz, D., & Díaz-Cuevas, P. (2025) · Energy, Sustainability and Society · doi:10.1186/s13705-025-00536-w · Cited by 13
“A complete review of this clustering technique can be found in prior research [62].”
Bus Arrival Time Prediction: A Comprehensive Review
Kumar, B. A., Singh, R., Shaji, H. E., & Vanajakshi, L. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3545695 · Cited by 13
“Though density-based clustering algorithms can identify clusters of any arbitrary shape, it is challenging to choose the density threshold [83] and is not used for time series clustering due to its high complexity [84].”
Zhen, J.-J., Lai, F., Shiau, J. S., Huang, M., Lu, Y., & Lin, J. (2025) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2024.12.018 · Cited by 13
A drift-aware dynamic ensemble model with two-stage member selection for carbon price forecasting
Zeng, L., Hu, H., Song, Q., Zhang, B.-T., Lin, R.-B., & Zhang, D. (2024) · Energy · doi:10.1016/j.energy.2024.133699 · Cited by 13
Time series clustering to improve one-class classifier performance
Machado, A. P. F., Munaro, C. J., Ciarelli, P. M., & Vargas, R. E. V. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122895 · Cited by 20
Climate change projection using statistical downscaling model over southern coastal Iran
Esfandeh, S., Danehkar, A., Salmanmahiny, A., Alipour, H., Kazemzadeh, M., Marcu, M. V., & Sadeghi, S. M. M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29416 · Cited by 14
“The primary objective of conducting time series analysis on a phenomenon is to develop a statistical model using historical data, thereby enabling the prediction of future outcomes for that specific phenomenon [60].”
BackgroundDynamic Multi-Network Mining of Tensor Time Series
Obata, K., Kawabata, K., Matsubara, Y., & Sakurai, Y. (2024) · The Web Conference · doi:10.1145/3589334.3645461 · Cited by 13
“Time series subsequence clustering [1, 51] is a useful unsupervised exploratory approach for recognizing dynamic changes and uncovering interesting patterns in time series.”
BackgroundModeling, Evaluating, and Applying the eWoM Power of Reddit Posts
Bonifazi, G., Corradini, E., Domenico Ursino, A., & Virgili, L. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010047 · Cited by 17
Zanotti, C., Rotiroti, M., Redaelli, A., Caschetto, M., Fumagalli, L., Stano, C., Sartirana, D., & Bonomi, T. (2022) · Water · doi:10.3390/w15010148 · Cited by 13
A new symbolic representation method for time series
Li, Y.-C., & Shen, D.-R. (2022) · Information Sciences · doi:10.1016/j.ins.2022.07.047 · Cited by 13
Operations Research Helps the Optimal Bidding of Virtual Power Plants
Kim, D., Cheon, H., Choi, D. G., & Im, S.-C. (2022) · INFORMS J. Appl. Anal. · doi:10.1287/inte.2022.1120 · Cited by 13
Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data
Yang, R., Wang, L., Tian, Q., Xu, N., & Yang, Y.-J. (2021) · Remote Sensing · doi:10.3390/rs13214426 · Cited by 13
“Traditional k-means clustering aims to partition n observations into k clusters and is widely used for solving time-series data clustering problems [40,41,58].”
BackgroundMethodLossless compression of industrial time series with direct access
Gómez-Brandón, A., Paramá, J. R., Villalobos, K., Illarramendi, A., & Brisaboa, N. (2021) · Computers in industry (Print) · doi:10.1016/j.compind.2021.103503 · Cited by 13
Noy, K., Ohana-Levi, N., Panov, N., Silver, M., & Karnieli, A. (2021) · Science of the Total Environment · doi:10.1016/j.scitotenv.2021.145154 · Cited by 13
Application of Agglomerative Hierarchical Clustering for Clustering of Time Series Data
Radovanović, A., Li, J., Milanović, J., Milosavljevic, N., & Storchi, R. (2020) · IEEE PES Innovative Smart Grid Technologies Conference Europe · doi:10.1109/isgt-europe47291.2020.9248759 · Cited by 13
“Due to increased interest in temporal data analysis in various areas, time series data clustering is gaining popularity [2], [3].”
BackgroundOnline QoS Prediction in the Cloud Environments Using Hybrid Time-Series Data Mining Approach
Keshavarzi, A., Toroghihaghighat, A., & Bohlouli, M. (2020) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-020-00371-z · Cited by 13
“There are various distance measures among time series in the literature (Aghabozorgi et al. 2015), such as DTW, PCC, Euclidean distance (ED), cross-correlation-based distances, and probability-based distance.”
BackgroundMethodInfluentialLi, L., Vrieling, A., Skidmore, A. K., & Wang, T. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124956 · Cited by 13
Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers
Halawa, M. S., Redondo, R. D. D., & Fernández-Vilas, A. (2020) · Italian National Conference on Sensors · doi:10.3390/s20154111 · Cited by 13
“Among all these approaches, machine learning (ML) stands out in analyzing time series data.”
BackgroundClustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping
Cao, Y.-Y., Yomo, T., & Ying, B.-W. (2020) · Microorganisms · doi:10.3390/microorganisms8030331 · Cited by 13
“As the Euclidean distance and Hausdorff distance were only available for the time series of identical lengths [17], DTW and DDTW were suitable for the analysis, considering the fact that the growth curves were often varied in time scale (length).”
BackgroundMethodBiclustering of Smart Building Electric Energy Consumption Data
Divina, F., Vela, F. L. G., & Torres, M. (2019) · Applied Sciences · doi:10.3390/app9020222 · Cited by 13
“A common analysis technique applied to time series is clustering [12,13].”
MethodEfficient Dynamic Time Warping for Big Data Streams
Martins, R. M., & Kerren, A. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8621878 · Cited by 9
“Many common data analysis and machine learning algorithms for time series, such as classification [1], clustering [4], or dimensionality reduction [5], require a distance measurement between pairs of time series in order to determine their similarity.”
Backgroundvan Delft, A., & Dette, H. (2018) · Bernoulli · doi:10.3150/20-bej1246 · Cited by 13
“The first two aspects are known to be crucial in terms of efficiency and accuracy of the solution and this is what most research focuses on (see section 5 of Aghabozorgi et al., 2015, for a full taxonomy of the different aspects of clustering time series). For instance, in parametric approaches clusters are usually bu…”
BackgroundMethodInfluentialTime-series clustering of cage-level sea lice data
Marques, A. R., Førde, H., & Revie, C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204319 · Cited by 13
“Here we extended clustering of time-series data for pattern discovery outside of the habitual biological field of application for gene expression data, for clustering cage-level sea lice count data [15].”
BackgroundMethodInfluentialMining and Predicting Temporal Patterns in the Quality Evolution of Wikipedia Articles
Zhang, H., Ren, Y.-Q., & Kraut, R. E. (2018) · Hawaii International Conference on System Sciences · doi:10.5465/ambpp.2018.13746abstract · Cited by 13
“In general, there are three categories of time-series clustering methods: shape-based, model-based and feature-based [30].”
BackgroundMethodRiyadi, M. A. A., Pratiwi, D., Irawan, A., & Fithriasari, K. (2017) · doi:10.26555/ijain.v3i3.98 · Cited by 13
Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations
Zhang, L.-Y., Pei, T., Meng, B., Lian, Y.-F., & Jin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3022625 · Cited by 13
“The available approaches for multivariate time series clustering can be divided into three main categories: model-based methods, feature-based meth-ods and shaped-based methods [7].”
MethodUnderstanding and Profiling CXL.mem Using PathFinder
Li, X., Guo, Z., Bai, Y., Ketkar, M., Wilkinson, H., & Liu, M. (2025) · Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication · doi:10.1145/3718958.3750479 · Cited by 12
Clustering Electricity Consumption Patterns using Functional Data Analysis
Seo, K., Na, H., Lee, W., Chen, C.-B., Kweon, S., Zhao, L., & Kumara, S. R. T. (2025) · Sustainable Energy, Grids and Networks · doi:10.1016/j.segan.2025.101742 · Cited by 12
Metadata
- Authors
- Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T.
- Affiliations
- University of Malaya (MY)
- ORCID
- Aghabozorgi, Saeed
- Venue
- Information Systems
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 53
- Pages
- 16-38
- ISSN
- 0306-4379 (ISSN-L)
- OpenAlex
- W1894414046
- Cited by
- 1,826
- References
- 280
- Citations by year
- 2026: 118 · 2025: 195 · 2024: 210 · 2023: 252 · 2022: 214 · 2021: 225 · 2020: 215 · 2019: 190 · 2018: 112 · 2017: 56 · 2016: 31 · 2015: 6
- FWCI
- 83.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Systems (publishedVersion)
- Funders
- University of Malaya Research (RP0061-13ICT)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Keywords
- time series clustering, unsupervised clustering, big data, clustering algorithms, pattern discovery, gene expression data, stock market analysis, clustering efficiency
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