{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T17:55:05Z","timestamp":1767117305197,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,12,14]],"date-time":"2017-12-14T00:00:00Z","timestamp":1513209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of the 13th Five-year Plan in China","award":["2016YFB1200401"],"award-info":[{"award-number":["2016YFB1200401"]}]},{"name":"Fund of Shanghai Cooperative Center for Maglev and Rail Transit"},{"name":"Fund of Shanghai Fei Tu You Science and Technology Company Limited","award":["0217013"],"award-info":[{"award-number":["0217013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD) and seasonal auto regressive integrated moving average (SARIMA) has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC), the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved.<\/jats:p>","DOI":"10.3390\/a10040139","type":"journal-article","created":{"date-parts":[[2017,12,14]],"date-time":"2017-12-14T04:30:55Z","timestamp":1513225855000},"page":"139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An EMD\u2013SARIMA-Based Modeling Approach for Air Traffic Forecasting"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4656-7040","authenticated-orcid":false,"given":"Wei","family":"Nai","sequence":"first","affiliation":[{"name":"Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing 314051, China"}]},{"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"Whitman School of Management, Syracuse University, Syracuse, NY 13244, USA"}]},{"given":"Shaoyin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing 314051, China"}]},{"given":"Decun","family":"Dong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Laik, M.N., Choy, M., and Sen, P. 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