Forecasting exotic vegetable wholesale prices using time series analysis methods

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dc.contributor.authorKate, Njoki Mbugua
dc.date.accessioned2022-02-02T13:16:31Z
dc.date.available2022-02-02T13:16:31Z
dc.date.issued2021
dc.descriptionSubmitted in partial fulfilment of the requirements for the Degree of Bachelor of Business Science Actuarial Science at Strathmore Universityen_US
dc.description.abstractVegetables are known to be highly perishable and seasonal in nature. Forecasting prices of vegetables is important to the Government of Kenya, the buyers and particularly to farmers as they can use this information to help them maximise their profits and minimise their losses. T11erefore, accurate forecasting of their prices requires the use of models that take the seasonal nature of vegetables into account. In this research paper, the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt-Winter's Exponential Smoothing (HWES) models were used to forecast the wholesale prices of kales and cabbages in Nairobi, Kenya using monthly price data from January 2012 to December 2019. T11e Augmented Dickey Fuller (ADF) test showed that both the kales and cabbages price data were stationary hence no need for differencing. SARIMA(1,0,0)(1,1,1)12model was the best forecasting model for cabbages. This model was selected amongst other SARIMA models as it had the least Akaike Information Criterion (AIC) value. T11e Holt-Winter's Exponential Smoothing method was the best for kales. Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were the forecast performance measures used to select the best forecasting model for kales and cabbages.en_US
dc.identifier.urihttp://hdl.handle.net/11071/12561
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.titleForecasting exotic vegetable wholesale prices using time series analysis methodsen_US
dc.typeUndergraduate projecten_US
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