| dc.description.abstract | Time Series Analysis has been used over the decades in data analysis and forecast
ing. Auto Regressive Integrated Moving Average (ARIMA) models have been fit on
economic data and engineering data. The models have also been used in analysis of
climate data. Previous studies have focussed on temperature data from National Mete
orological Stations where summarized monthly values were used. In this study, we used
daily rainfall data from Kenya Meteorological Services Station in Kisumu. The objec
tives included univariate time series modelling using ARIMA on long term rainfall data
for daily, monthly, seasonal and annual data and forecasting rainfall for the different time
periods. The other objective was to compare forecast from univariate ARIMA to Vector
Autoregression (VAR) when rainfall, minimum and maximum temperature values are
included in model. ARIMA models were fit on the KMS rainfall data, and VAR models
were fit on temperature, minimum and maximum rainfall data from KMS. Finally, farm
ers’ local rainfall data was compared to that of KMS for independence. Results showed
that forecasts under VAR did not give a more precise forecast of future rainfall than
ARIMA. Further, that there was not enough statistically significant evidence to suggest
that rainfall data from KMS and farmers’ locale were independent. | en_US |
