{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T20:06:28Z","timestamp":1746129988533,"version":"3.38.0"},"reference-count":29,"publisher":"China Science Publishing & Media Ltd.","issue":"3","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":163,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>The total electricity consumption (TEC) can accurately reflect the operation of the national economy, and the forecasting of the TEC can help predict the economic development trend, as well as provide insights for the formulation of macro policies. Nowadays, high-frequency and massive multi-source data provide a new way to predict the TEC. In this paper, a \u201cseasonal-cumulative temperature index\u201d is constructed based on high-frequency temperature data, and a mixed-frequency prediction model based on multi-source big data (Mixed Data Sampling with Monthly Temperature and Daily Temperature index, MIDAS-MT-DT) is proposed. Experimental results show that the MIDAS-MT-DT model achieves higher prediction accuracy, and the \u201cseasonal-cumulative temperature index\u201d can improve prediction accuracy.<\/jats:p>","DOI":"10.1162\/dint_a_00215","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T15:27:49Z","timestamp":1686670069000},"page":"750-766","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":2,"title":["Total Electricity Consumption Forecasting Based on Temperature Composite Index and Mixed-Frequency Models"],"prefix":"10.3724","volume":"5","author":[{"given":"Xuerong","family":"Li","sequence":"first","affiliation":[{"name":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at 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