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TSclust: An R Package for Time Series Clustering

Citations

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Cited by:

  1. Amato, Federico & Laib, Mohamed & Guignard, Fabian & Kanevski, Mikhail, 2020. "Analysis of air pollution time series using complexity-invariant distance and information measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
  2. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
  3. Mantas Svazas & Valentinas Navickas & Yuriy Bilan & Joanna Nakonieczny & Jana Spankova, 2021. "Biomass Clusterization from a Regional Perspective: The Case of Lithuania," Energies, MDPI, vol. 14(21), pages 1-15, October.
  4. Szczepocki Piotr, 2019. "Clustering Companies Listed on the Warsaw Stock Exchange According to Time-Varying Beta," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(2), pages 63-79, June.
  5. Beibei Zhang & Rong Chen, 2018. "Nonlinear Time Series Clustering Based on Kolmogorov-Smirnov 2D Statistic," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 394-421, October.
  6. Sirin, Selahattin Murat & Uz, Dilek & Sevindik, Irem, 2022. "How do variable renewable energy technologies affect firm-level day-ahead output decisions: Evidence from the Turkish wholesale electricity market," Energy Economics, Elsevier, vol. 112(C).
  7. Ding Ding & Chong Guan & Calvin M. L. Chan & Wenting Liu, 2020. "Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-21, December.
  8. Tommaso Agasisti & Ekaterina Abalmasova & Ekaterina Shibanova & Aleksei Egorov, 2019. "The Causal Impact Of Performance-Based Funding On University Performance: Quasi-Experimental Evidence From A Policy In Russian Higher Education," HSE Working papers WP BRP 221/EC/2019, National Research University Higher School of Economics.
  9. Costa, Antonio & da Silva, Cristiano & Matos, Paulo, 2022. "The Brazilian financial market reaction to COVID-19: A wavelet analysis," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 13-29.
  10. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
  11. Lucio Palazzo & Riccardo Ievoli, 2023. "Detecting Regional Differences in Italian Health Services during Five COVID-19 Waves," Stats, MDPI, vol. 6(2), pages 1-13, April.
  12. Cimmino, Francesco & Mastelic, Joelle & Genoud, Stephane, 2016. "Multi-Method Approach to Compare the Socio-Demographic Typology of Residents and Clusters of Electricity Load Curves in a Swiss Sustainable Neighbourhood," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2016), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016, pages 310-314, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
  13. Steinmann, Patrick & Auping, Willem L. & Kwakkel, Jan H., 2020. "Behavior-based scenario discovery using time series clustering," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
  14. Juan José Fernández-Durán & María Mercedes Gregorio-Domínguez, 2021. "Consumer Segmentation Based on Use Patterns," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 72-88, April.
  15. Carolina Euán & Hernando Ombao & Joaquín Ortega, 2018. "The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 71-99, April.
  16. Sokhna Dieng & Pierre Michel & Abdoulaye Guindo & Kankoe Sallah & El-Hadj Ba & Badara Cissé & Maria Patrizia Carrieri & Cheikh Sokhna & Paul Milligan & Jean Gaudart, 2020. "Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies," IJERPH, MDPI, vol. 17(11), pages 1-23, June.
  17. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
  18. Bertsch, Valentin & Devine, Mel & Sweeney, Conor & Parnell, Andrew C., 2018. "Analysing long-term interactions between demand response and different electricity markets using a stochastic market equilibrium model," Papers WP585, Economic and Social Research Institute (ESRI).
  19. Jasmien Lismont & Tine Van Calster & María Óskarsdóttir & Seppe vanden Broucke & Bart Baesens & Wilfried Lemahieu & Jan Vanthienen, 2019. "Closing the Gap Between Experts and Novices Using Analytics-as-a-Service: An Experimental Study," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(6), pages 679-693, December.
  20. Nadine Baudot-Trajtenberg & Itamar Caspi, 2018. "Measuring the importance of global factors in determining inflation in Israel," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and deglobalisation, volume 100, pages 183-208, Bank for International Settlements.
  21. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
  22. Frank Davenport & Chris Funk, 2015. "Using time series structural characteristics to analyze grain prices in food insecure countries," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 7(5), pages 1055-1070, October.
  23. Carlo Drago & Andrea Scozzari, 2022. "Evaluating conditional covariance estimates via a new targeting approach and a networks-based analysis," Papers 2202.02197, arXiv.org.
  24. Hua Chen & Shuang Dai & Fanlin Meng, 2023. "Smart Building Thermal Management: A Data-Driven Approach Based on Dynamic and Consensus Clustering," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
  25. Hanjo Odendaal & Monique Reid & Johann F. Kirsten, 2020. "Media‐Based Sentiment Indices as an Alternative Measure of Consumer Confidence," South African Journal of Economics, Economic Society of South Africa, vol. 88(4), pages 409-434, December.
  26. Druica, Elena & Goschin, Zizi, 2016. "Does Economic Status Matter for the Regional Variation of Malnutrition-Related Diabetes in Romania? Temporal Clustering and Spatial Analyses," MPRA Paper 88831, University Library of Munich, Germany.
  27. Benny Ren & Ian Barnett, 2022. "Autoregressive mixture models for clustering time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 918-937, November.
  28. Carlo Drago & Andrea Scozzari, 2023. "A Network-Based Analysis for Evaluating Conditional Covariance Estimates," Mathematics, MDPI, vol. 11(2), pages 1-19, January.
  29. Carmela Iorio & Gianluca Frasso & Antonio D’Ambrosio & Roberta Siciliano, 2023. "Boosted-oriented probabilistic smoothing-spline clustering of series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1123-1140, October.
  30. Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.
  31. Achilleas Anastasiou & Peter Hatzopoulos & Alex Karagrigoriou & George Mavridoglou, 2021. "Causality Distance Measures for Multivariate Time Series with Applications," Mathematics, MDPI, vol. 9(21), pages 1-15, October.
  32. Matos, José M.A. & Ramos, Sandra & Costa, Vítor, 2019. "Stochastic simulated rents in Portuguese public-private partnerships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 107-117.
  33. Dongjun Kim & Jinsung Yun & Kijung Kim & Seungil Lee, 2021. "A Comparative Study of the Robustness and Resilience of Retail Areas in Seoul, Korea before and after the COVID-19 Outbreak, Using Big Data," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
  34. M. Isabel Landaluce-Calvo & Juan I. Modroño-Herrán, 2020. "Classification for Time Series Data. An Unsupervised Approach Based on Reduction of Dimensionality," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 380-398, July.
  35. Stephen L. France & Yuying Shi, 2017. "Aggregating Google Trends: Multivariate Testing and Analysis," Papers 1712.03152, arXiv.org, revised Mar 2018.
  36. Roberto Benedetti & Federica Piersimoni & Giacomo Pignataro & Francesco Vidoli, 2020. "Identification of spatially constrained homogeneous clusters of COVID‐19 transmission in Italy," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1169-1187, December.
  37. Jerónimo Chirivella-Martorell & Álvaro Briz-Redón & Ángel Serrano-Aroca, 2018. "Modelling of Biomass Concentration, Multi-Wavelength Absorption and Discrimination Method for Seven Important Marine Microalgae Species," Energies, MDPI, vol. 11(5), pages 1-13, April.
  38. Zuokas, Danas & Gul, Evren & Lim, Alvin, 2022. "How did COVID-19 change what people buy: Evidence from a supermarket chain," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
  39. Margherita Gerolimetto & Stefano Magrini, 2022. "Weighting in clustering time series: an application to Covid-19 data," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 76(4), pages 4-12, October-D.
  40. Chong Guan & Wenting Liu & Jack Yu-Chao Cheng, 2022. "Using Social Media to Predict the Stock Market Crash and Rebound amid the Pandemic: The Digital ‘Haves’ and ‘Have-mores’," Annals of Data Science, Springer, vol. 9(1), pages 5-31, February.
  41. Tianbo Chen & Ying Sun & Carolina Euan & Hernando Ombao, 2021. "Clustering Brain Signals: a Robust Approach Using Functional Data Ranking," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 425-442, October.
  42. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting," Complexity, Hindawi, vol. 2018, pages 1-21, April.
  43. María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez & Antonio Guillamón & Alberto Falces & Ana García-Garre & Antonio Gabaldón, 2019. "Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation," Energies, MDPI, vol. 13(1), pages 1-31, December.
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