{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T05:25:01Z","timestamp":1740115501806,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>The aim of this research study is to explore the forecasting technique to fit to the existing waste data of a tape converting production factory using a time series method. Optimal type of time series techniques to minimise the error between actual and forecasted data was chosen after comparing three types of time series techniques. The data analysis, based on the accuracy of their outputs, resulted in the &amp;ldquo;Double Exponential Smoothing&amp;rdquo; being the preferable choice since the error was less than that for other techniques. The projected forecasted amounts were 13.09, 11.08, 11.77 and 10.25 (Unit of measurement is 10,000 kilograms) for January, February, March and April 2015. After benchmarking error percentages (MAPE) that against similar techniques and problems from other references,the error in this study (18%) was less than the benchmarking source (21% [9] and 33% [10] respectively). Therefore this technique is more accurate than the benchmarking techniques by 17% and 83% respectively. After rechecking the actual data with forecast time series data, the average MAPE was found to be around 15% which is still lower that the errors quoted in other reference papers.<\/jats:p>","DOI":"10.3233\/978-1-61499-720-7-396","type":"book-chapter","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:06:08Z","timestamp":1740053168000},"source":"Crossref","is-referenced-by-count":0,"title":["Time Series Analysis and Forecasting Technique for Converting Industrial Waste Management: Case Study of a Tape Converting Production in Thailand"],"prefix":"10.3233","author":[{"family":"Lertpocasombut Krittiya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Sriploy Supawut","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Information Modelling and Knowledge Bases XXVIII"],"original-title":[],"deposited":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:10:42Z","timestamp":1740053442000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-719-1&spage=396&doi=10.3233\/978-1-61499-720-7-396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-720-7-396","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2017]]}}}