{"created":"2020-03-08T15:38:12.561068+00:00","id":1282,"links":{},"metadata":{"_buckets":{"deposit":"787eeeb0-c220-4efa-bb8e-af18da8823cd"},"_deposit":{"id":"1282","owners":[],"pid":{"revision_id":0,"type":"recid","value":"1282"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/1282","sets":["1582963436320","1582963436320:1582965763232"]},"author_link":[],"communities":["yueco"],"control_number":"1282","item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"FORECASTING OF RAINFALL IN CENTRAL DRY ZONE IN MYANMAR USING SARIMA MODEL (Thiri Aung, 2019)","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Rainfall is one of the most important sources of water on earth supporting the\r existence of the majority of living organisms. A time series analysis, modeling and\r forecasting constitutes a tool of importance with reference to a wide range of scientific\r purposes in Meteorology. Examples is precipitation, humidity, temperature, solar\r radiation, floods and draught. This study research applies the Box-Jenkins\r methodology, employing SARIMA (Seasonal Autoregressive Integrated Moving\r Average) model to perform modeling past rainfall time series components structure and\r predicting further rainfall in according to the past. This methodology are capable of\r representing stational as well as nonstationary time series. The model is mostly fit to\r both show the past rainfall data and thus generate the most reliable further forecasted is\r selected by the R2, RMSE and BIC for model evaluation criteria. This paper explores\r the application of the Box-Jenkins approach to Rainfall data series in Chauk and\r Shwebo Townships in Myanmar. As the statistical characteristic for stochastic seasonal\r models are also investigated and the fitted model for monthly rainfall data series in\r Chauk and Shwebo Township in Central Dry Zone of Myanmar from January, 2006 to\r December, 2018. The monthly rainfall date are found by the following the four states\r of model building, identification, estimation, diagnostic checking and forecasting. The\r statistical package, SPSS software and EViews software was used to build models for\r the above two series."}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2020-05-05"}],"displaytype":"preview","filename":"Thiri Aung (MAS - 51).pdf","filesize":[{"value":"1091 Kb"}],"format":"application/pdf","licensetype":"license_0","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/1282/files/Thiri Aung (MAS - 51).pdf"},"version_id":"ad68f071-5a75-4e68-925c-fc3ccc477c64"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_journal_title":"Yangon University of Economics"}]},"item_1583103233624":{"attribute_name":"Thesis/dissertations","attribute_value_mlt":[{"subitem_awarding_university":"Yangon University of Economics","subitem_supervisor(s)":[{"subitem_supervisor":"Prof.Dr. Maw Maw Khin"}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Thiri Aung"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Other"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Thesis"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2019-12-01"},"item_title":"FORECASTING OF RAINFALL IN CENTRAL DRY ZONE IN MYANMAR USING SARIMA MODEL (Thiri Aung, 2019)","item_type_id":"21","owner":"1","path":["1582963436320","1582965763232"],"publish_date":"2020-03-05","publish_status":"0","recid":"1282","relation_version_is_last":true,"title":["FORECASTING OF RAINFALL IN CENTRAL DRY ZONE IN MYANMAR USING SARIMA MODEL (Thiri Aung, 2019)"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2024-04-01T08:46:18.197321+00:00"}