Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/14047
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dc.contributor.authorMathur, Gautam
dc.contributor.authorGoyal, Tarang
dc.contributor.authorMuramalla, Neelima
dc.date.accessioned2015-07-10T04:26:37Z
dc.date.available2015-07-10T04:26:37Z
dc.date.issued2015
dc.identifier.citationMathur, G., Goyal, T., & Muramalla, N.. (2015). Forecasting Daily Transactions Volume for Financial Planning and Budgeting. 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. Indian Institute of Management, Ahmedabaden_US
dc.identifier.urihttp://hdl.handle.net/11718/14047
dc.description.abstractAppropriate transactions volume prediction is very crucial for making different Strategic and Tactical decisions at various management levels in any field. This paper analyzes the possible reasons behind daily transaction volume fluctuations for Bill Payment (BPay) and different factors influencing it. A forecasting solution around the Decomposition Concept of observed/explainable factors was developed, and a statistical Unobserved Components Model (UCM) was fit on the decomposed transactions volume line to forecast daily volumes for BPay in future time periods. Models were built for three main channels – Electronic, Paper Managed and Paper Unmanaged. The results show that error percentage (when forecasted volume was compared to actual volume) was less than 1% for Electronic and Paper Managed Channels whereas for Paper Unmanaged, the error was 3.6%.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management, Ahmedabaden_US
dc.relation.ispartofseriesIC 15;062
dc.subjectDecomposition Modelen
dc.subjectUnobserved Components Model (UCM)en
dc.subjectTime-Seriesen
dc.subjectNonlongitudinal modelen
dc.titleForecasting Daily Transactions Volume for Financial Planning and Budgetingen_US
dc.typeArticleen_US
Appears in Collections:4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

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