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Increasingly, modern data scales necessitate lossy compression techniques, where exactness is sacrificed for a smaller compressed representation. One challenge in lossy compression is that different applications may have different accuracy demands. Today's compression techniques struggle in this setting either forcing the user to compress at the strictest accuracy demand, or to re-encode the data at multiple resolutions. This paper proposes a simple, but effective multiresolution compression algorithm for time series data, where a single encoding can effectively be decompressed at multiple output resolutions. There are a number of benefits over current state-of-the-art techniques for time series compression. (1) The storage footprint of this encoding is smaller than re-encoding the data at multiple resolutions. (2) Similarly, the compression latency is generally smaller than re-encoding at multiple resolutions. (3) Finally, the decompression latency of our encoding is significantly faster than single encodings at the strictest accuracy demand.<\/jats:p>","DOI":"10.1145\/3588953","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T17:42:05Z","timestamp":1685468525000},"page":"1-26","source":"Crossref","is-referenced-by-count":14,"title":["Hierarchical Residual Encoding for Multiresolution Time Series Compression"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7438-5634","authenticated-orcid":false,"given":"Bruno","family":"Barbarioli","sequence":"first","affiliation":[{"name":"The University of Chicago, Chicago, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4302-0525","authenticated-orcid":false,"given":"Gabriel","family":"Mersy","sequence":"additional","affiliation":[{"name":"The University of Chicago, Chicago, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2114-8886","authenticated-orcid":false,"given":"Stavros","family":"Sintos","sequence":"additional","affiliation":[{"name":"University of Illinois Chicago, Chicago, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6968-4090","authenticated-orcid":false,"given":"Sanjay","family":"Krishnan","sequence":"additional","affiliation":[{"name":"The University of Chicago, Chicago, IL, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 4439--4445","author":"Akcora Cuneyt G","year":"2021","unstructured":"Cuneyt G Akcora, Yitao Li, Yulia R Gel, and Murat Kantarcioglu. 2021. 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