Formalization and Scalable Processing of Spatially-Embedded Time Series

Formalization and Scalable Processing of Spatially-Embedded Time Series | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Formalization and Scalable Processing of Spatially-Embedded Time Series Carlos Quijada-Fuentes, M. Andrea Rodriguez, Diego Seco This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7358749/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Time series play a crucial role in numerous scientific and technological domains.In many cases, these series do not come from completely independent sensors, but are associated with specific locations in a space—such as geographic space—inducing common spatial patterns. In this work, we study different datasets sharing this characteristic: meteorological records over a spatial grid, traffic sensors distributed across the city of Madrid, and electroencephalograms where sensor positions reflect the distribution of electrodes on the human scalp. We provide a general formalization of the key properties of these datasets with the aim of facilitating their analysis in similar contexts. We evaluate properties such as temporal and spatio-temporal autocorrelation, and propose variants of compact data structures adapted to each domain type. Compared to state-of-the-art approaches, our proposals show competitive results, particularly highlighting spatial efficiency for dense datasets with high spatio-temporal locality, as well as query times for some cases of window-based access. Time Series Spatio-Temporal Data Compact Data Structures Auto-correlation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7358749","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512591442,"identity":"1e99405b-3a4f-4adb-a83e-b13396ee26c4","order_by":0,"name":"Carlos Quijada-Fuentes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYLCCBB4Gxgb2BgYJEIePeC08ByBa2Ii1iLFBIoFILfzth7dJPJCxk+2f+cbwxocaBnmCWiTOpJVJJPAkG8+4nWNsOeMYg2EbIS0GDDlmQC3MiQ23c8ykeRsYEgjaYsD/BqSlPnH+zTNm0n+J0iIBtuVw4oYbPGbSjMRokbjxrNgigee48cYzacWWPcckCPuFvz95482fPdWy844f3njjR42NPD8hLSC3MTD2IGwlQgNIC8MPohSOglEwCkbBSAUAZVU6Gu6TReoAAAAASUVORK5CYII=","orcid":"","institution":"University of Concepción","correspondingAuthor":true,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Quijada-Fuentes","suffix":""},{"id":512591443,"identity":"d57ccb05-9382-4fb9-ade4-f8c50fb02311","order_by":1,"name":"M. 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