{"_buckets": {"deposit": "c5a663f0-6704-420d-ad76-0599735086ca"}, "_deposit": {"created_by": 20, "id": "8958", "owner": "20", "owners": [20], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "8958"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00008958"}, "author_link": [], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "A Seasonal Time Seriws Model of Production Transformers in Hitachi Soe Electric and Machinery Co.,Ltd(2013-2017)", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Many economic time series exhibit seasonal behaviour. The estimation of\nseasonal variation is important problem in time series analysis. Consequently,\nseasonal variations are needed to determine and seasonal adjustments are needed in\nforecasting. In this thesis, the production series for transforners for the period of\nJanuary 2013 to December 2017 are studied.\nStochastic models for monthly production series are found by using Box-\nJenkins model building approach. Basic statistical characteristics for the production\nseries are first investigated and statistical test for seasonality is applied to each series\nto confirm the existence of seasonality. Seasonal variation of the production series for\ntransformers from January 2013 to December 2017 are measured by using Ratio to\nMoving Average Method. Suitable stochastic models for monthly production series\nare found by following the three stages of model building, namely, identification,\nestimation and diagnostic checking. Whenever needed, computer programs for the\nsystematic development of the model building procedure are developed. It is found\nthat ARIMA (1,0, 0) x (0,1,0)D, ARIMA (1,0, 0) x (1,1,0)12 ARIMA (1, 1,0) x\n(1,7,0)pmodels are suitable for our series. Forecasting is very important in future\ndecisions making. The forecast based on the fitted model were also validated in this\nthesis in order to support future decision making for planning pu{pose."}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2023-07-06"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Yu Wah Hlaing,M.Econ(Statistics)-3.pdf", "filesize": [{"value": "6.4 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 6400000.0, "url": {"url": "https://meral.edu.mm/record/8958/files/Yu Wah Hlaing,M.Econ(Statistics)-3.pdf"}, "version_id": "d89ab505-6d36-4bca-bf9a-3dbcea791f7e"}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "Yangon University of Economics", "subitem_supervisor(s)": [{"subitem_supervisor": "U Win Min Thant"}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Yu Wah Hlaing"}]}]}, "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": "2018-05-01"}, "item_title": "A Seasonal Time Seriws Model of Production Transformers in Hitachi Soe Electric and Machinery Co.,Ltd(2013-2017)", "item_type_id": "21", "owner": "20", "path": ["1582963436320", "1582965462498"], "permalink_uri": "https://meral.edu.mm/records/8958", "pubdate": {"attribute_name": "Deposit date", "attribute_value": "2023-07-06"}, "publish_date": "2023-07-06", "publish_status": "0", "recid": "8958", "relation": {}, "relation_version_is_last": true, "title": ["A Seasonal Time Seriws Model of Production Transformers in Hitachi Soe Electric and Machinery Co.,Ltd(2013-2017)"], "weko_shared_id": -1}