{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T14:49:15Z","timestamp":1792853355470,"version":"4.5.0"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"10","license":[{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["814496"],"award-info":[{"award-number":["814496"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>Smart objects are increasingly widespread and their ecosystem, also known as the Internet of Things (IoT), is relevant in many application scenarios. The huge amount of temporally annotated data produced by these smart devices demands efficient techniques for the transfer and storage of time series data. Compression techniques play an important role toward this goal and, even though standard compression methods could be used with some benefit, there exist several ones that specifically address the case of time series by exploiting their peculiarities to achieve more effective compression and more accurate decompression in the case of lossy compression techniques. This article provides a state-of-the-art survey of the principal time series compression techniques, proposing a taxonomy to classify them considering their overall approach and their characteristics. Furthermore, we analyze the performances of the selected algorithms by discussing and comparing the experimental results that were provided in the original articles.<\/jats:p>\n                  <jats:p>The goal of this article is to provide a comprehensive and homogeneous reconstruction of the state-of-the-art, which is currently fragmented across many articles that use different notations and where the proposed methods are not organized according to a classification.<\/jats:p>","DOI":"10.1145\/3560814","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T08:38:13Z","timestamp":1661935093000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":100,"title":["Time Series Compression Survey"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0248-1974","authenticated-orcid":false,"given":"Giacomo","family":"Chiarot","sequence":"first","affiliation":[{"name":"Department of Environmental Sciences, Informatics, and Statistics of Ca\u2019 Foscari University of Venice, Venice, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9909-1808","authenticated-orcid":false,"given":"Claudio","family":"Silvestri","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, Informatics, and Statistics of Ca\u2019 Foscari University of Venice, Venice, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2018.12.008"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.07.193"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2740932"},{"key":"e_1_3_4_5_2","unstructured":"D. Salomon. 2007. Data Compression: The Complete Reference (4th. ed.). Springer."},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/33.6.518"},{"key":"e_1_3_4_7_2","doi-asserted-by":"crossref","unstructured":"K. Sayood. 2006. Introduction to Data Compression (3rd. ed.). Morgan Kaufmann.","DOI":"10.1016\/B978-012620862-7\/50018-3"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2014.7004244"},{"key":"e_1_3_4_9_2","article-title":"Online learning for matrix factorization and sparse coding","volume":"11","author":"Mairal J.","year":"2009","unstructured":"J. Mairal, F. Bach, J. Ponce, and G. Sapiro. 2009. Online learning for matrix factorization and sparse coding Journal of Machine Learning Research 11, (2009), 19\u201360.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/78.258082"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005580"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/322344.322346"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/WCNCW.2018.8368985"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/GTSD.2018.8595614"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-014-0368-8"},{"key":"e_1_3_4_16_2","volume-title":"Proceedings of the 19th International Conference on Data Engineering.","author":"Lazaridisand I.","year":"2003","unstructured":"I. Lazaridisand and S. Mehrotra. 2003. Capturing sensor-generated time series with quality guarantees. In Proceedings of the 19th International Conference on Data Engineering. 429\u2013440."},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2006.875394"},{"key":"e_1_3_4_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02574699"},{"issue":"6","key":"e_1_3_4_19_2","article-title":"Algorithm for compressing time-series data","volume":"36","author":"Hawkins S. E. I.","year":"2012","unstructured":"S. E. I. Hawkins and E. H. Darlington. 2012. Algorithm for compressing time-series data NASA Tech Briefs 36, 6 (2012), 7\u20138. Retrieved from https:\/\/ntrs.nasa.gov\/search.jsp?R=20120010460.","journal-title":"NASA Tech Briefs"},{"issue":"343","key":"e_1_3_4_20_2","article-title":"On chebyshev polynomials and their applications","volume":"1","author":"Lv X.","year":"2017","unstructured":"X. Lv and S. Shen. 2017. On chebyshev polynomials and their applications Advances in Difference Equations 1, 343 (2017), 1\u20136.","journal-title":"Advances in Difference Equations"},{"key":"e_1_3_4_21_2","volume-title":"Proceedings of the Discrete Wavelet Transforms - Theory and Applications","author":"Abo-Zahhad M.","year":"2011","unstructured":"M. Abo-Zahhad. 2011. ECG signal compression using discrete wavelet transform. In Proceedings of the Discrete Wavelet Transforms - Theory and Applications. J. T. Olkkonen (Ed.), InTech."},{"key":"e_1_3_4_22_2","volume-title":"MIT Press.","author":"Goodfellow I.","year":"2016","unstructured":"I. Goodfellow, Y. Bengio, and A. Courville. 2016. Deep Learning MIT Press. Retrieved from http:\/\/www.deeplearningbook.org."},{"key":"e_1_3_4_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306"},{"key":"e_1_3_4_24_2","unstructured":"D. Hsu. Time series compression based on adaptive piecewise recurrent autoencoder. arXiv:1707.07961. Retrieved from https:\/\/arxiv.org\/abs\/1707.07961."},{"key":"e_1_3_4_25_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_4_26_2","volume-title":"Proceedings of the Encyclopedia of Big Data Technologies","author":"Suel T.","year":"2018","unstructured":"T. Suel. 2018. Delta compression techniques. In Proceedings of the Encyclopedia of Big Data Technologies. S. Sakr and A. Zomaya (Eds.), Springer International Publishing."},{"key":"e_1_3_4_27_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1235\/1\/012107"},{"key":"e_1_3_4_28_2","volume-title":"Proceedings of the Dateso 2010 Annual International Workshop on DAtabases, TExts, Specifications and Objects, Stedronin-Plazy, Czech Republic","author":"Walder J.","year":"2010","unstructured":"J. Walder, M. Kr\u00e1tk\u00fd, and J. Platos. 2010. Fast fibonacci encoding algorithm. In Proceedings of the Dateso 2010 Annual International Workshop on DAtabases, TExts, Specifications and Objects, Stedronin-Plazy, Czech Republic. J. Pokorn\u00fd, V. Sn\u00e1sel, and K. Richta (Eds.), April 21\u201323, 2010, 72\u201383. CEUR-WS.org. Retrieved from http:\/\/ceur-ws.org\/Vol-567\/paper14.pdf."},{"key":"e_1_3_4_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/APEIE.2018.8546121"},{"key":"e_1_3_4_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3264903"},{"key":"e_1_3_4_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/INDIN.2018.8471921"},{"key":"e_1_3_4_32_2","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO.2017.8081677"},{"key":"e_1_3_4_33_2","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2010.505800"},{"key":"e_1_3_4_34_2","volume-title":"Proceedings of the IBPSA-AIRAH Building Simulation Conference.","author":"Goldstein R.","year":"2011","unstructured":"R. Goldstein, M. Glueck, and A. Khan. 2011. Real-time compression of time series building performance data. In Proceedings of the IBPSA-AIRAH Building Simulation Conference."},{"key":"e_1_3_4_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2003.1248854"},{"key":"e_1_3_4_36_2","volume-title":"Microsoft.","author":"Steve Y.","year":"2006","unstructured":"Y. Steve, E. Gunnar, G. Mark, H. Thomas, K. Dan, L. Xunying Andrew, M. Gareth, O. Julian, O. Dave, V. Valtcho, and W. Phil. 2006. The HTK Book Microsoft."},{"key":"e_1_3_4_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2010.51"},{"key":"e_1_3_4_38_2","volume-title":"AGU Fall Meeting Abstracts","author":"Smith K.","year":"2008","unstructured":"K. Smith, D. Depolo, J. Torrisi, N. Edwards, G. Biasi, and D. Slater. 2008. The 2008 mw 6.0 wells, nevada earthquake sequence AGU Fall Meeting Abstracts (2008)."},{"key":"e_1_3_4_39_2","doi-asserted-by":"publisher","DOI":"10.3390\/s140610146"},{"key":"e_1_3_4_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/2141622.2141637"},{"key":"e_1_3_4_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.10.037"},{"key":"e_1_3_4_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107507"},{"key":"e_1_3_4_43_2","doi-asserted-by":"publisher","DOI":"10.1090\/S0025-5718-1965-0178586-1"},{"key":"e_1_3_4_44_2","volume-title":"Proceedings of the International Conference on Electronic Devices, Systems, and Applications.","author":"Ali M.","year":"2011","unstructured":"M. Ali, R. Borgo, and M. W. Jones. 2011. Wavelet transform and fast fourier transform for signal compression: A comparative study. In Proceedings of the International Conference on Electronic Devices, Systems, and Applications. 280\u2013285."},{"key":"e_1_3_4_45_2","volume-title":"Proceedings of the IEEE Transactions on Signal Processing.","author":"Huang Y. M.","year":"1999","unstructured":"Y. M. Huang and J. L. Wu. 1999. A refined fast 2-d discrete cosine transform algorithm. In Proceedings of the IEEE Transactions on Signal Processing. 904\u2013907."},{"key":"e_1_3_4_46_2","volume-title":"International Workshop on Tracking, Telemetry and Command Systems for Space Applications (TTC\u201916)","author":"Mess J. G.","unstructured":"J. G. Mess, R. Schmidt, G. Fey, and F. Dannemann. On the compression of spacecraft housekeeping data using discrete cosine transforms. In International Workshop on Tracking, Telemetry and Command Systems for Space Applications (TTC\u201916). 1\u20138."},{"key":"e_1_3_4_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/ATIT49449.2019.9030505"},{"key":"e_1_3_4_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/DCC50243.2021.00023"},{"key":"e_1_3_4_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/568518.568520"},{"key":"e_1_3_4_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/CAC48633.2019.8996842"},{"key":"e_1_3_4_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/882082.882086"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3560814","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3560814","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T15:00:34Z","timestamp":1750172434000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3560814"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,2]]},"references-count":50,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10,31]]}},"alternative-id":["10.1145\/3560814"],"URL":"https:\/\/doi.org\/10.1145\/3560814","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,2]]},"assertion":[{"value":"2021-09-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-08-21","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}