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dc.contributor.authorNgige, Isabel Wanjiru
dc.date.accessioned2021-09-14T09:26:40Z
dc.date.available2021-09-14T09:26:40Z
dc.date.issued2020-03
dc.identifier.urihttp://hdl.handle.net/11071/12145
dc.descriptionA Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Statistical Sciences (MSc. SS) at Strathmore Universityen_US
dc.description.abstractBackground: Gross Domestic Product (GDP) is the market value of goods and services produced within a selected geographical area usually a coun- try in a selected interval in time often a year and can be measured and forecasted in di erent ways for use by governments and other market par- ticipants.Speci c users of information on GDP analysis include the United Nations0 Sustainable Development Goal assessment whose key indicator is economic growth as measured by GDP and the joint International Mon- etary Fund-World Bank methodology for conducting standardized debt- sustainability analyses in low-income countries. Objective:The main objective of this study was to assess the superiority as suggested by Literature of a Hybrid Autoregressive Integrated Moving Average(ARIMA) and feed forward Arti cial Neural Network (ANN) model over a pure ARIMA model in forecasting Kenya0s GDP. Methods: The ARIMA and the additive ANN-ARIMA Hybrid model is used to forecast absolute GDP values and the comparative forecast accuracy is tested using the RMSE and visualization plots.The Box-Jenkins method- ology is used to t the ARIMA model while the feed-forward Neural Network Autoregressive(NNAR) structure is used to model the neural network por- tion of the hybrid model .en_US
dc.language.isoen_USen_US
dc.publisherStrathmore Universityen_US
dc.subjectAutoregressive Integrated Moving Averageen_US
dc.subjectArti cial Neural Networken_US
dc.subjectGross Domestic Producten_US
dc.subjectNeural Network Autoregressiveen_US
dc.titleForecasting Kenya’s GDP using a hybrid neural network and ARIMA modelen_US
dc.typeThesisen_US


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