{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T10:16:10Z","timestamp":1769249770774,"version":"3.49.0"},"reference-count":15,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Macroeconomic forecasting is a critical task for policymakers, financial institutions, and businesses, enabling informed decision\u2010making and strategic planning. Traditional econometric models predominantly rely on official statistical indicators, which are often published with significant lags and limited frequency, creating a fundamental challenge of data scarcity. The recent proliferation of the Internet of Things (IoT) has opened new avenues for economic measurement by generating massive, high\u2010frequency data that captures real\u2010time economic activity. Studies have demonstrated the value of using alternative data streams, such as satellite imagery, search engine queries, and maritime traffic, as proxies for traditional macroeconomic indicators. This paper proposes Meta\u2010IoTNet, a novel framework that combines IoT data with meta\u2010learning for few\u2010shot prediction of macroeconomic indicators. Unlike traditional MAML\u2010based methods and existing IoT\u2010based forecasting approaches, Meta\u2010IoTNet incorporates multi\u2010scale temporal feature extraction to capture both short\u2010term fluctuations and long\u2010term trends in IoT data. Additionally, our framework integrates task\u2010aware context encoding, which adapts the model to new tasks with minimal data, making it particularly effective in volatile economic conditions. Through extensive experiments, Meta\u2010IoTNet demonstrates significant improvements over existing models, especially in data\u2010scarce environments.<\/jats:p>","DOI":"10.1002\/itl2.70209","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:10:21Z","timestamp":1768565421000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging\n                    <scp>IoT<\/scp>\n                    Information Sensing and Meta\u2010Learning for Few\u2010Shot Prediction of Macroeconomic Indicators"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2210-4358","authenticated-orcid":false,"given":"Sisheng","family":"Wan","sequence":"first","affiliation":[{"name":"Xinyang Vocational and Technical College  Henan China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1198\/073500102317351921"},{"issue":"3","key":"e_1_2_7_3_1","first-page":"172","article-title":"The Scarcity of Economic Data: A Macroeconomic Perspective","volume":"33","author":"Giustinelli P.","year":"2019","journal-title":"Journal of Economic Perspectives"},{"issue":"7","key":"e_1_2_7_4_1","first-page":"97","article-title":"That \u2018Internet of Things\u2019 Thing","volume":"22","author":"Ashton K.","year":"2009","journal-title":"RFID Journal"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1257\/jep.30.4.171"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-4932.2012.00809.x"},{"issue":"5","key":"e_1_2_7_7_1","article-title":"Tracking the Economic Impact of COVID\u201019 and Recovery in Real Time Using English Channel Shipping Movements","volume":"119","author":"Mall A.","year":"2022","journal-title":"National Academy of Sciences of the United States of America"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3477494"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2025.3591368"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3375920"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2025.3563943"},{"key":"e_1_2_7_12_1","article-title":"Deep Transformer Models For Time Series Forecasting: The Influenza Prevalence Case","author":"Wu N.","year":"2021","journal-title":"arXiv preprint"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2021.03.012"},{"key":"e_1_2_7_14_1","first-page":"1126","article-title":"Model\u2010Agnostic Meta\u2010Learning for Fast Adaptation of Deep Networks","volume":"70","author":"Finn C.","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"e_1_2_7_15_1","article-title":"Meta\u2010Learning for Few\u2010Shot Time Series Forecasting","author":"Chen X.","year":"2020","journal-title":"arXiv preprint"},{"key":"e_1_2_7_16_1","unstructured":"C.Borio P.Disyatat M.Juselius andP.Rungcharoenkitkul \u201cMacroeconomic Modelling: Retrospect and Prospect \u201dBIS Working Paper. 2019(816)."}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70209","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/itl2.70209","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70209","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T03:40:55Z","timestamp":1769139655000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.70209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":15,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1002\/itl2.70209"],"URL":"https:\/\/doi.org\/10.1002\/itl2.70209","archive":["Portico"],"relation":{},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"value":"2476-1508","type":"print"},{"value":"2476-1508","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-09-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70209"}}