Time-series clustering – A decade review
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Time-series clustering methods are commonly divided into three classes: model-based, feature-based, and shape-based approaches.
“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 <scp>AI</scp> ‐assisted writing (2026) · cites this paper as Aghabozorgi et al., 2015
Cites
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Absalom, E. E., Ikotun, A. M., Oyelade, O. N., Abualigah, L., Agushaka, J., Eke, C. I., & Akinyelu, A. A. (2022) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2022.104743 · Cited by 894 · see citing works
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“Large, high-dimensional spatiotemporal data cannot be managed by existing data reduction algorithms within a tolerable time frame and acceptable quality (Aghabozorgi, Seyed Shirkhorshidi, and Ying Wah 2015; García, Luengo, and Herrera 2015).”
BackgroundBig data preprocessing: methods and prospects
García, S., Ramírez‐Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016) · Big Data Analytics · doi:10.1186/s41044-016-0014-0 · Cited by 717 · see citing works
“Developments on real-time applications can be also found in the literature [103].”
BackgroundThe Zwicky Transient Facility: Science Objectives
Graham, M., Kulkarni, S., Bellm, E., Adams, S., Barbarino, C., Blagorodnova, N., Bodewits, D., Bolin, B., Brady, P., Cenko, S., et al. (2019) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ab006c · Cited by 613 · see citing works
“Methods for time series clustering exist in other research areas such as statistics, finance, medicine, and economics, but not all of these new methods have made it to astronomy (for a review of time series clustering methods, see Aghabozorgi et al. 2015).”
MethodLin, C., Boursier, E., & Papakonstantinou, Y. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3384345.3384357 · Cited by 583 · see citing works
“More precisely, the user provides (i) a time series T , (ii) a choice between fixed-length segmentation algorithm [14, 20] and variable-length/window-based segmentation algorithm [34, 28, 29, 5, 22, 39] , and (iii) the identifier of a compression function family (e.g., Polynomial function family).”
Complex network approaches to nonlinear time series analysis
Zou, Y., Donner, R. V., Marwan, N., Donges, J. F., & Kurths, J. (2018) · Physics Reports · doi:10.1016/j.physrep.2018.10.005 · Cited by 636 · see citing works
“From the perspective of time axis, time series clustering can be classified into three categories, whole time series clustering, subsequence clustering and time point clustering [11].”
BackgroundBandara, K., Bergmeir, C., & Smyl, S. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112896 · Cited by 506 · see citing works
“Also, many such measures mostly focus on the shape of the respective time series (Aghabozorgi et al., 2015), and they may yield unintuitive results and limited interpretability (Wang et al.”
BackgroundA Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data
Shirkhorshidi, A. S., Aghabozorgi, S., & Wah, T. Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144059 · Cited by 382 · see citing works
“Examples of distance-based clustering algorithms include partitioning clustering algorithms, such as k-means as well as k-medoids and hierarchical clustering [17].”
MethodSelf-citationTeichgraeber, H., & Brandt, A. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.02.012 · Cited by 251
“Other methods are popular in general time-series clustering but – to the best of our knowledge – have not been widely applied to find representative periods for energy systems optimization [22].”
MethodA Review on Time Series Aggregation Methods for Energy System Models
Hoffmann, M., Kotzur, L., Stolten, D., & Robinius, M. (2020) · Energies · doi:10.3390/en13030641 · Cited by 227 · see citing works
“• A method to choose representatives [59].”
BackgroundInfluentialMachine Learning for Clinical Outcome Prediction
Shamout, F. E., Zhu, T.-T., & Clifton, D. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3007816 · Cited by 210 · see citing works
“Those features can be categorized into four types: data adaptive, non-data adaptive, modelbased and data-dictated approaches [41].”
BackgroundAdaptively constrained dynamic time warping for time series classification and clustering
Li, H., Liu, J.-X., Yang, Z.-L., Liu, R. W., Wu, K., & Wan, Y. (2020) · Information Sciences · doi:10.1016/j.ins.2020.04.009 · Cited by 201
“Time series classification and clustering are important for data mining of moving object trajectories [1, 50].”
BackgroundAn overview of clustering methods with guidelines for application in mental health research
Gao, C. X., Dwyer, D. E., Zhu, Y., Smith, C. L., Du, L., Filia, K., Bayer, J., Menssink, J. M., Wang, T., Bergmeir, C., et al. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115265 · Cited by 213 · see citing works
A comparative study of clustering techniques for electrical load pattern segmentation
Rajabi, A., Eskandari, M., Ghadi, M. J., Li, L., Zhang, J.-F., & Siano, P. (2020) · Renewable & Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109628 · Cited by 192 · see citing works
A modeler's guide to handle complexity in energy system optimization
Kotzur, L., Nolting, L., Hoffmann, M., Groß, T., Smolenko, A., Priesmann, J., Büsing, H., Beer, R., Kullmann, F., Singh, B., et al. (2020) · doi:10.1016/j.adapen.2021.100063 · Cited by 188
“In the realm of time series clustering, this means that “homogeneous time series data are grouped together based on a certain similarity measure” [94].”
BackgroundYang, J.-J., Ning, C., Deb, C., Zhang, F., Cheong, D., Lee, S., Sekhar, C., & Tham, K. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.03.071 · Cited by 186 · see citing works
Time series k-means: A new k-means type smooth subspace clustering for time series data
Huang, X., Ye, Y.-M., Xiong, L., Lau, R., Jiang, N., & Wang, S. (2016) · Information Sciences · doi:10.1016/j.ins.2016.05.040 · Cited by 180
Shape-Based Approach to Household Electric Load Curve Clustering and Prediction
Teeraratkul, T., O’Neill, D., & Lall, S. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2683461 · Cited by 178 · see citing works
“Clustering is a technique where similar data are placed into the same group without prior knowledge of groups definition [6].”
BackgroundMethodInfluentialTime-Series Clustering in R Using the dtwclust Package
Sarda-Espinosa, A. (2019) · The R Journal · doi:10.32614/rj-2019-023 · Cited by 163 · see citing works
“The most common approaches are hierarchical and partitional clustering (cf. Table 4 in Aghabozorgi et al. (2015)), the latter of which includes fuzzy clustering.”
BackgroundMethodInfluentialLiu, X., Ding, Y., Tang, H., & Xiao, F. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110601 · Cited by 181 · see citing works
A Benchmark Study on Time Series Clustering
Javed, A., Lee, B., & Rizzo, D. (2020) · Machine Learning with Applications · doi:10.1016/j.mlwa.2020.100001 · Cited by 149 · see citing works
“Examples include stock market ticker price data [1], gene expression sequence data [1], sensor-generated data, CPU usage data, and network monitoring data.”
BackgroundExploiting spatiotemporal patterns for accurate air quality forecasting using deep learning
Lin, Y., Mago, N., Gao, Y., Li, Y., Chiang, Y.-Y., Shahabi, C., & Ambite, J. (2018) · SIGSPATIAL/GIS · doi:10.1145/3274895.3274907 · Cited by 146
“However, traditional cluster methods like K-means do not work for high dimensional time series data [2].”
BackgroundMethodTemporal Data Meets LLM - Explainable Financial Time Series ForecastingPreprint
Yu, X., Chen, Z., Ling, Y., Dong, S., Liu, Z., & Lu, Y.-B. (2023) · arXiv.org · doi:10.48550/arxiv.2306.11025 · Cited by 145 · see citing works
“The current approaches include time-series correlation analysis [8, 11, 20, 48] and clustering [1, 3, 50].”
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain FeaturesPreprint
Madiraju, N., Sadat, S. M., Fisher, D., & Karimabadi, H. (2018) · arXiv.org · doi:10.48550/arxiv.1802.01059 · Cited by 143 · see citing works
“This has left a gap in technology for accurate unsupervised learning of time series data which encompass many areas of science and engineering such as financial trading, medical monitoring, and event detection (Aghabozorgi et al., 2015).”
BackgroundTime works well: Dynamic time warping based on time weighting for time series data mining
Li, H. (2021) · Information Sciences · doi:10.1016/j.ins.2020.08.089 · Cited by 142
Deep Time-Series Clustering: A Review
Alqahtani, A., Ali, M., Xie, X., & Jones, M. W. (2021) · Electronics · doi:10.3390/electronics10233001 · Cited by 133 · see citing works
“There are two main kinds of hierarchical clustering methods—agglomerative (bottom-up) and divisive (top-down) [74,139].”
BackgroundInfluentialApproaches and Applications of Early Classification of Time Series: A Review
Gupta, A., Gupta, H. P., Biswas, B., & Dutta, T. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2020.3027279 · Cited by 123 · see citing works
“With these properties, numerous data mining algorithms have been developed to study various aspects of time series such as indexing [13], forecasting [14], [15], clustering [16], and classification [17].”
MethodClustering of residential electricity customers using load time series
Motlagh, O., Berry, A., & O’Neil, L. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.12.063 · Cited by 129 · see citing works
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Gao, N., Xue, H., Shao, W., Zhao, S., Qin, K. K., Prabowo, A., Rahaman, M. S., & Salim, F. D. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3474838 · Cited by 127 · see citing works
“Can we cluster groups of time series with similar temporal and spatial paerns [5, 85]? Can we impute”
BackgroundDetection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
Tsyganskaya, V., Martinis, S., Marzahn, P., & Ludwig, R. (2018) · Remote Sensing · doi:10.3390/rs10081286 · Cited by 125 · see citing works
“The combination of segmentation techniques with SAR time-series data can be used to discover multi-temporal characteristics and patterns, allowing for the extraction of useful information from enormous and complex data sets [44] and empowering the derivation of time series features with the advantages of an object-bas…”
MethodHistorical energy security performance in EU countries
Matsumoto, K., Doumpos, M., & Andriosopoulos, K. D. (2018) · doi:10.1016/j.rser.2017.06.058 · Cited by 122
Multivariate time series clustering based on common principal component analysis
Li, H. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.060 · Cited by 121
Li, S.-W., Zhang, C.-P., Du, J., Cong, X.-W., Zhang, L.-J., Jiang, Y., & Wang, L.-Y. (2022) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2022.100009 · Cited by 117 · see citing works
Huang, P., Wen, C., Fu, L., Peng, Q., & Tang, Y. (2019) · Information Sciences · doi:10.1016/j.ins.2019.12.053 · Cited by 132 · see citing works
A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques
Obthong, M., Tantisantiwong, N., Jeamwatthanachai, W., & Wills, G. (2020) · International Conference on Finance, Economics, Management and IT Business · doi:10.5220/0009340700630071 · Cited by 115 · see citing works
Han, L., Yang, G., Yang, H. F., Xu, B., Li, Z., & Yang, X. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01638 · Cited by 119 · see citing works
“The definition of clusters depends on the user with obvious subjectivity (Aghabozorgi et al., 2015), and thus clustering evaluations can also be rather subjective.”
BackgroundCausal Discovery from Temporal Data: An Overview and New Perspectives
Gong, C., Zhang, C., Yao, D., Bi, J., Li, W., & Xu, Y. (2024) · ACM Computing Surveys · doi:10.1145/3705297 · Cited by 69 · see citing works
“Different approaches have been proposed for different tasks such as classification[1, 2], clustering[3, 4], prediction[5], causal discovery[6, 7], etc.”
Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation
Ferreira, G. O., Ravazzi, C., Dabbene, F., Calafiore, G., & Fiore, M. (2023) · IEEE Access · doi:10.1109/access.2023.3236261 · Cited by 106 · see citing works
“According to Aghabozorgi et al. [24], evaluation metrics are divided into two groups: external and internal indexes.”
BackgroundMethodIdentifying At-Risk Students for Early Interventions—A Time-Series Clustering Approach
Hung, J.-L., Wang, M. C., Wang, S., Abdelrasoul, M., Li, Y., & He, W. (2015) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2015.2504239 · Cited by 108 · see citing works
“…[42]), (8) applications in information search (2.3%, 8 articles, [43]), (9) review of time-series clustering literature (2.0%, 7 articles, e.g. [44]), (10) applications in social trend research (1.7%, 6 articles, e.g. [45]), (11) time-series data visualization (1.4%, 5 articles, e.g. [46],…”
InfluentialMikalsen, K. Ø., Bianchi, F., Soguero-Ruíz, C., & Jenssen, R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.11.030 · Cited by 104 · see citing works
“Keywords: Multivariate time series, Similarity measures, Kernel methods, Missing data, Gaussian mixture models, Ensemble learning”
MethodClustering patterns in efficiency and the coming-of-age of the cryptocurrency market
Sigaki, H. Y. D., Perc, M., & Ribeiro, H. (2019) · Scientific Reports · doi:10.1038/s41598-018-37773-3 · Cited by 96 · see citing works
Unsupervised Clustering-Based Short-Term Solar Forecasting
Feng, C., Cui, M., Hodge, B., Lu, S.-Y., Hamann, H., & Zhang, J. (2019) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2018.2881531 · Cited by 95 · see citing works
Salient Subsequence Learning for Time Series Clustering
Zhang, Q., Wu, J., Zhang, P., Long, G., & Zhang, C.-Q. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2847699 · Cited by 94 · see citing works
“Shapelets also have been used to cluster time series [10], [36], [37].”
MethodA review and evaluation of elastic distance functions for time series clustering
Holder, C., Middlehurst, M., & Bagnall, A. J. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01952-0 · Cited by 105 · see citing works
“Nevertheless, there have been numerous comparative studies (for example (Aghabozorgi et al., 2015) and (Javed et al.”
BackgroundDynamic clustering analysis for driving styles identification
de Zepeda, M. V. N., Meng, F.-L., Su, J.-Y., Zeng, X.-J., & Wang, Q. (2021) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2020.104096 · Cited by 86
“Time series clustering has been used in different fields including biology, energy, finances and psychology to discover complex time evolving patterns [18].”
MethodMetabolic rhythms in flowing waters: An approach for classifying river productivity regimes
Savoy, P. R., Appling, A., Heffernan, J. B., Stets, E. G., Read, J. S., Harvey, J. W., & Bernhardt, E. S. (2019) · Limnology and Oceanography · doi:10.1002/lno.11154 · Cited by 104 · see citing works
“…(1) rawdata–based approaches that utilize the full time series, (2) feature-based approaches that reduce data dimensionality prior to computing dissimilarity measures, and (3) model-based approaches that define dissimilarity based on differences in model parameters (Aghabozorgi et al. 2015).”
BackgroundInfluentialA Data-Driven Approach for Discovery of Heat Load Patterns in District Heating
Calikus, E., Nowaczyk, S., Sant'Anna, A., Gadd, H., & Werner, S. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113409 · Cited by 85 · see citing works
“Clustering is the task of organizing data where similar objects are placed into related or homogeneous groups without prior knowledge of the groups definitions [23].”
BackgroundEnd-to-end deep representation learning for time series clustering: a comparative study
Lafabrègue, B., Weber, J., Gançarski, P., & Forestier, G. (2021) · Data mining and knowledge discovery · doi:10.1007/s10618-021-00796-y · Cited by 84 · see citing works
“Most of the existing methods consist in applying a standard clustering method but uses either a specific dissimilarity measure or a time series representation (Aghabozorgi et al. 2015).”
MethodRobust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection
Li, Z., Zhao, Y., Liu, R., & Pei, D. (2018) · International Workshop on Quality of Service · doi:10.1109/iwqos.2018.8624168 · Cited by 78 · see citing works
“methods [7, 8] usually calculate similarity directly based on the raw data and hence cannot detect coherent clusters for real-world KPIs.”
BackgroundClustering and Classification for Time Series Data in Visual Analytics: A Survey
Ali, M., Alqahtani, A., Jones, M. W., & Xie, X. (2019) · IEEE Access · doi:10.1109/access.2019.2958551 · Cited by 92 · see citing works
“The divisive algorithm (top-down) starts by grouping all objects into one cluster then divides the cluster until each object is in a separate cluster [12], [152].”
BackgroundMethodInfluentialPattern recognition in building energy performance over time using energy benchmarking data
Papadopoulos, S., Bonczak, B., & Kontokosta, C. E. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.03.079 · Cited by 76 · see citing works
Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting
Yu, X., Chen, Z., & Lu, Y.-B. (2023) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-industry.69 · Cited by 75 · see citing works
“The current approaches include time-series correlation analysis (Plerou et al., 1999; Gopikrishnan et al., 2000; Conlon et al., 2009; Chen et al., 2018) and clustering (Rani and Sikka, 2012; Babu et al., 2012; Aghabozorgi et al., 2015).”
MethodUnsupervised Deep Learning for IoT Time Series
Liu, Y., Zhou, Y., Yang, K., & Wang, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3243391 · Cited by 78 · see citing works
“In contrast, they only investigate specific machine learning (ML) tasks, such as anomaly detection [12], [19], [20], [21], [22], [23], classification [24], [25], clustering [26], [27], [28], [29], [30], [31], and prediction [32], [33].”
BackgroundSPONGE: A generalized eigenproblem for clustering signed networks
Cucuringu, M., Davies, P., Glielmo, A., & Tyagi, H. (2019) · International Conference on Artificial Intelligence and Statistics · doi:10.48550/arxiv.1904.08575 · Cited by 74
“Another application stems from time series analysis, in particular clustering time series [3], a task broadly used for analyzing gene expression data in biology [20], economic time series that capture macroeconomic variables [19], and financial time series corresponding to large baskets of instruments in the stock mar…”
BackgroundSingh, J., & Singh, D. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102799 · Cited by 71 · see citing works
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
Lee, C., & van der Schaar, M. (2020) · International Conference on Machine Learning · doi:10.48550/arxiv.2006.08600 · Cited by 72
“To address these challenges, there have been various attempts to find a good representation with reduced dimensionality or to define a proper similarity measure for times-series (Aghabozorgi et al., 2015).”
BackgroundReal time transit demand prediction capturing station interactions and impact of special events
Noursalehi, P., Koutsopoulos, H. N., & Zhao, J. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.10.023 · Cited by 71 · see citing works
Effective and Real-time In-App Activity Analysis in Encrypted Internet Traffic Streams
Liu, J., Fu, Y., Ming, J., Ren, Y., Sun, L., & Xiong, H. (2017) · Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098049 · Cited by 71 · see citing works
“Most recently, a group of data mining researchers [1, 6, 16, 17, 23, 24] segment time series with clustering based algorithms in different time series analysis problems, however, most of these clustering based segmentation algorithms require a pre-specified number of segments and a set of complex…”
BackgroundXu, L., Deng, Z., Xie, Y., Lin, X.-K., & Hu, X.-S. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2022.3212024 · Cited by 70 · see citing works
“methods because of its efficiency and simplicity [23].”
MethodOkawa, M. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107699 · Cited by 70
Kang, R., Park, B., Eady, M., Ouyang, Q., & Chen, K.-J. (2020) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2020.127789 · Cited by 70 · see citing works
Planakis, N., Papalambrou, G., & Kyrtatos, N. (2022) · Applied Energy · doi:10.1016/j.apenergy.2021.118085 · Cited by 69
Assessing road transport sustainability by combining environmental impacts and safety concerns
Wang, D. D. (2019) · doi:10.1016/j.trd.2019.10.022 · Cited by 69
Transforming Complex Problems into K-means Solutions
Liu, H., Chen, J., Dy, J., & Fu, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3237667 · Cited by 67 · see citing works
“Researchers also use K-means and its variants to cluster time-series data [1], [130].”
MethodBehavior segmentation of electricity consumption patterns: A cluster analytical approach
Kaur, R., & Gabrijelcic, D. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109236 · Cited by 67
Deep learning for clustering of multivariate clinical patient trajectories with missing values
de Jong, J., Emon, M. A., Wu, P., Karki, R., Sood, M., Godard, P., Ahmad, A., Vrooman, H. A., Hofmann‐Apitius, M., & Fröhlich, H. (2019) · GigaScience · doi:10.1093/gigascience/giz134 · Cited by 65 · see citing works
Microbial communities across activated sludge plants show recurring species-level seasonal patterns
Peces, M., Dottorini, G., Nierychlo, M., Andersen, K. S., Dueholm, M. K. D., & Nielsen, P. (2022) · ISME Communications · doi:10.1038/s43705-022-00098-4 · Cited by 65 · see citing works
“To decrease the dimensionality inherently contained in time-series data [40], each species time-series was decomposed into trend, season and residual components based on local smoothing regression [41].”
MethodComparing and Combining Time Series Trajectories Using Dynamic Time Warping
Vaughan, N., & Gabrys, B. (2016) · International Conference on Knowledge-Based Intelligent Information & Engineering Systems · doi:10.1016/j.procs.2016.08.106 · Cited by 65
“Time-series are dynamic data and a current research challenge is to recognize dynamic changes in time series 7 .”
BackgroundThe trilemma among CO2 emissions, energy use, and economic growth in Russia
Magazzino, C., Mele, M., Drago, C., Kuşkaya, S., Pozzi, C., & Monarca, U. (2023) · Scientific Reports · doi:10.1038/s41598-023-37251-5 · Cited by 64 · see citing works
Review of Clustering Methods for Functional Data
Zhang, M.-M., & Parnell, A. C. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3581789 · Cited by 63
“[3] for time series, and Atluri et al.”
BackgroundClustering-enhanced stock price prediction using deep learning
Li, M., Zhu, Y., Shen, Y., & Angelova, M. N. (2022) · World Wide Web · doi:10.1007/s11280-021-01003-0 · Cited by 69 · see citing works
“Distance-based functions are suitable to capture similarities between short time series [1], which can be divided into two categories, non-elastic Lp norm distance and elastic distance functions.”
BackgroundTime-series clustering based on linear fuzzy information granules
Duan, L., Yu, F., Pedrycz, W., Wang, X., & Yang, X.-Y. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.09.032 · Cited by 62
Fast Constrained Dynamic Time Warping for Similarity Measure of Time Series Data
Choi, W., Cho, J., Lee, S., & Jung, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3043839 · Cited by 62
“Time series data mining is utilized in numerous applications such as clustering, classification [1]–[3] fault detection [4], [5], pattern recognition [6], and prediction [7].”
MethodDeep learning for time series classification
Fawaz, H. I. (2020) · theses.fr (ABES) · doi:10.70675/4be65ba1z3327z4573zb045z15c1d1435785 · Cited by 9 · see citing works
Park, H., & Jung, J.-Y. (2020) · Expert systems with applications · doi:10.1016/j.eswa.2019.112950 · Cited by 60
Fontes, C., & Pereira, O. (2016) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2015.11.005 · Cited by 60 · see citing works
“There are some works about pattern recognition in univariate time series (Liao, 2005; Keogh and Kasetty, 2002; Aghabozorgi et al., 2015) using standard approaches (Liao, 2005; Trebuňa and Halčinová, 2013).”
MethodAn Embedded Deep-Clustering-Based Load Profiling Framework
Eskandarnia, E., Al-Ammal, H., & Ksantini, R. (2021) · Sustainable cities and society · doi:10.1016/j.scs.2021.103618 · Cited by 59 · see citing works
Cowton, J., Kyriazakis, I., Plötz, T., & Bacardit, J. (2018) · Italian National Conference on Sensors · doi:10.3390/s18082521 · Cited by 59 · see citing works
“However, methods falling into this category are typically unable to strike a reasonable balance between quality of analysis and speed due to the innate characteristics of time-series data [13].”
BackgroundRobust fuzzy clustering of multivariate time trajectories
D’Urso, P., Giovanni, L. D., & Massari, R. (2018) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2018.05.002 · Cited by 63 · see citing works
“The clustering of univariate and multivariate time series could be undertaken adopting different approaches (for a more detailed review, see [53,1,6,3]):”
MethodIndex‐Based Characterization and Quantification of Groundwater Dynamics
Heudorfer, B., Haaf, E., Stahl, K., & Barthel, R. (2019) · Water Resources Research · doi:10.1029/2018wr024418 · Cited by 58
“Simultaneously, the five components introduced by Poff (1997) were combined with concepts from classical time series analysis, that is, “structure‐level” and “shape‐level” characteristics (Aghabozorgi et al., 2015).”
MethodCommunity detection in networks without observing edges
Hoffmann, T., Peel, L., Lambiotte, R., & Jones, N. S. (2018) · Science Advances · doi:10.1126/sciadv.aav1478 · Cited by 58 · see citing works
“More broadly, this work is related to the problem of series clustering (23), whose purpose is to take a set of time series as input and to group them according to ameasure of similarity.”
BackgroundMonitoring worker fatigue using wearable devices: A case study to detect changes in gait parameters
Baghdadi, A., Cavuoto, L., Jones-Farmer, A., Rigdon, S., Esfahani, E., & Megahed, F. (2019) · Journal of QualityTechnology · doi:10.1080/00224065.2019.1640097 · Cited by 57 · see citing works
Zhu, H.-H., Wang, X., Chen, X.-Q., & Zhang, L.-Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103178 · Cited by 56 · see citing works
Data driven battery anomaly detection based on shape based clustering for the data centers class
Haider, S. N., Zhao, Q., & Li, X.-L. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101479 · Cited by 56 · see citing works
Katsuki, M., Tatsumoto, M., Kimoto, K., Iiyama, T., Tajima, M., Munakata, T., Miyamoto, T., & Shimazu, T. (2023) · Headache · doi:10.1111/head.14482 · Cited by 55
Detecting voids in 3D printing using melt pool time series data
Mahato, V., Obeidi, M. A., Brabazon, D., & Cunningham, P. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01694-8 · Cited by 64 · see citing works
Di, Z., Chang, M., & Guo, P.-K. (2019) · Water · doi:10.3390/w11020339 · Cited by 55 · see citing works
Time-Series Clustering Based on the Characterization of Segment Typologies
Guijo-Rubio, D., Durán-Rosal, A. M., Gutiérrez, P. A., Troncoso, A., & Hervás-Martínez, C. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2962584 · Cited by 55
“Time series clustering can be approached by considering specific distance measures for time series combined with standard clustering techniques [12], [4].”
BackgroundMethodInfluentialA P-spline based clustering approach for portfolio selection
Iorio, C., Frasso, G., D'Ambrosio, A., & Siciliano, R. (2018) · Expert systems with applications · doi:10.1016/j.eswa.2017.11.031 · Cited by 55
Kim, A., Oh, K., Jung, J.-Y., & Kim, B. (2017) · International journal of computer integrated manufacturing (Print) · doi:10.1080/0951192x.2017.1407447 · Cited by 55
Liu, H.-M., Zhan, Q., Yang, C., & Wang, J. (2018) · Remote Sensing · doi:10.3390/rs10040654 · Cited by 54
Continuous in vivo Metabolism by NMR
Judge, M. T., Wu, Y., Tayyari, F., Hattori, A., Glushka, J., Ito, T., Arnold, J., & Edison, A. S. (2019) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2019.00026 · Cited by 53 · see citing works
“Additionally, statistical tests can be formulated through these approaches to compare metabolic status between conditions (Leng and Müller, 2005; Febrero-Bande and de la Fuente, 2012; Aghabozorgi et al., 2015).”
A lane-changing risk profile analysis method based on time-series clustering
Chen, T.-Y., Shi, X., & Wong, Y. (2021) · doi:10.1016/j.physa.2020.125567 · Cited by 53
Liu, H.-M., Zhan, Q., Yang, C., & Wang, J. (2019) · Science of the Total Environment · doi:10.1016/j.scitotenv.2018.10.252 · Cited by 53 · see citing works
FEDD: Feature Extraction for Explicit Concept Drift Detection in time series
Cavalcante, R. C., Minku, L. L., & Oliveira, A. (2016) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn.2016.7727274 · Cited by 53 · see citing works
“Guajardo et al. [10] proposed an implicit concept drift handling method for time series which is based on moving window and support vector regression (SVR).”
MethodLazzari, F., Mor, G., Cipriano, J., Gabaldon, E., Grillone, B., Chemisana, D., & Solsona, F. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.02.260 · Cited by 53 · see citing works
Esmaeilzadeh, S., Salehi, A., Hetz, G., Olalotiti-Lawal, F., Darabi, H., & Castiñeira, D. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2019.106485 · Cited by 53 · see citing works
The Bengal Water Machine: Quantified freshwater capture in Bangladesh
Shamsudduha, M., Taylor, R. G., Haq, M. I., Nowreen, S., Zahid, A., & Ahmed, K. M. (2022) · Science · doi:10.1126/science.abm4730 · Cited by 59 · see citing works
Comprehensive Process Drift Detection with Visual Analytics
Yeshchenko, A., Ciccio, C. D., Mendling, J., & Polyvyanyy, A. (2019) · International Conference on Conceptual Modeling · doi:10.1007/978-3-030-33223-5_11 · Cited by 52 · see citing works
“used as a subroutine of other more complex algorithms and is employed as a standard tool in data science for anomaly detection, character recognition, pattern discovery, visualization of time series [4]. Change point detection is a technique to detect the points in which multivariate time series exhibit changes in the…”
MethodInfluentialDynamic portfolio optimization with inverse covariance clustering
Wang, Y., & Aste, T. (2021) · Expert systems with applications · doi:10.2139/ssrn.4092344 · Cited by 51
“Subsequent clustering uses a sliding window to capture a period of data points and analyze for recurrent patterns [64, 65].”
- 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 · 2013: 1 · 2012: 1
- 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 › Signal Processing › 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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