PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Inventions10.3390/inventions704009420227494A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series ApproachFahad Radhi Alharbi, Denes Csalahttps://www.mdpi.com/2411-5134/7/4/94/pdf
IOP Conference Series: Materials Science and Engineering10.1088/1757-899x/407/1/0121482018407012148Forecasting Tourist Visits Using Seasonal Autoregressive Integrated Moving Average MethodR Fahrudinhttp://stacks.iop.org/1757-899X/407/i=1/a=012148/pdf, http://stacks.iop.org/1757-899X/407/i=1/a=012148?key=crossref.94ee41437993b0936795d098ac60f97d, http://stacks.iop.org/1757-899X/407/i=1/a=012148/pdf, http://stacks.iop.org/1757-899X/407/i=1/a=012148?key=crossref.94ee41437993b0936795d098ac60f97d
2020 8th International Conference on Information and Communication Technology (ICoICT)10.1109/icoict49345.2020.91664232020Sea Level Prediction by Using Seasonal Autoregressive Integrated Moving Average Model, Case Study in Semarang, IndonesiaRonald Tulus, Didit Adytia, Nugrahinggil Subasita, Dede Tarwidihttp://xplorestaging.ieee.org/ielx7/9162155/9166148/09166423.pdf?arnumber=9166423
Jurnal Matematika, Statistika dan Komputasi10.20956/j.v18i1.14284202118178-92Inflation Forecasting for East Kalimantan Province Using Hybrid Singular Spectrum Analysis- Autoregressive Integrated Moving Average ModelMelisa Arumsari, Sri Wahyuningsih, Meiliyani Siringoringohttps://journal.unhas.ac.id/index.php/jmsk/article/download/14284/7307, https://journal.unhas.ac.id/index.php/jmsk/article/download/14284/7307
Environmental Management and Sustainable Development10.5296/emsd.v7i1.12566201871115Forecasting Rainfall in Mauritius using Seasonal Autoregressive Integrated Moving Average and Artificial Neural NetworksJayrani Cheeneebash, Ashvin Harradon, Ashvin Gopaulhttp://www.macrothink.org/journal/index.php/emsd/article/viewFile/12566/9972, http://www.macrothink.org/journal/index.php/emsd/article/viewFile/12566/9972
International Journal of Medical Science and Public Health10.5455/ijmsph.2020.1028504112019201901Employing seasonal autoregressive integrated moving average forecasting model to predict the number of dengue cases in MumbaiSujata Pol, Shekahr Rajderkar, Seema Gokhe, Kamlesh Sutharhttps://www.ejmanager.com/fulltextpdf.php?mno=46038
MEDIA STATISTIKA10.14710/medstat.14.1.44-55202114144-55AUTOREGRESSIVE FRACTIONAL INTEGRATED MOVING AVERAGE (ARFIMA) MODEL TO PREDICT COVID-19 PANDEMIC CASES IN INDONESIAPuspita Kartikasari, Hasbi Yasin, Di Asih I Maruddanihttps://ejournal.undip.ac.id/index.php/media_statistika/article/viewFile/32951/19681, https://ejournal.undip.ac.id/index.php/media_statistika/article/viewFile/32951/19681
Annals of Epidemiology10.1016/j.annepidem.2014.10.0152015252101-106Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average modelXujun Zhang, Yuanyuan Pang, Mengjing Cui, Lorann Stallones, Huiyun Xianghttps://api.elsevier.com/content/article/PII:S1047279714004578?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S1047279714004578?httpAccept=text/plain
Journal of Physics: Conference Series10.1088/1742-6596/1503/1/012002202015031012002Forecasting Farmer Exchange Rate in Bali Province Using Seasonal Autoregressive Integrated Moving Average (SARIMA) MethodDinda Pratiwi, Sisilia M.U. Agustini, Wiwin Windasari, Eka N. Kencanahttps://iopscience.iop.org/article/10.1088/1742-6596/1503/1/012002/pdf, https://iopscience.iop.org/article/10.1088/1742-6596/1503/1/012002, https://iopscience.iop.org/article/10.1088/1742-6596/1503/1/012002, https://iopscience.iop.org/article/10.1088/1742-6596/1503/1/012002/pdf, https://iopscience.iop.org/article/10.1088/1742-6596/1503/1/012002/pdf, https://iopscience.iop.org/article/10.1088/1742-6596/1503/1/012002/pdf, https://iopscience.iop.org/article/10.1088/1742-6596/1503/1/012002, https://iopscience.iop.org/article/10.1088/1742-6596/1503/1/012002/pdf
Data in Brief10.1016/j.dib.2021.106759202135106759Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methodsChristopher J. Lynch, Ross Gorehttps://api.elsevier.com/content/article/PII:S2352340921000433?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S2352340921000433?httpAccept=text/plain