| dc.description.abstract | ABSTRACT
Time series is a measured observation recorded with time. This statistical procedure
is applicable in many fields of study including engineering and economics. The process of
collecting data sometimes faces a lot of challenges that may arise due to defective working
tools, misplaced or lost records and errors that are prone to occur. These problems can be
addressed by estimating the missing values so as to enable one to proceed with the analysis
and forecasting. The most commonly used approaches include the use of autoregressivemoving
average models developed by Box Jenkins, use of extrapolation or interpolation
under regression analysis and use of state space models where data is considered as a
combination of level, trend and seasonal components. This project intends to use the most
appropriate method of estimating missing values by using the direct method of imputation.
Incomplete secondary data obtained from the Ministry of fisheries and Development,
together with the Kenya Marine and Fisheries Research Institute are to be used to estimate
the gap left just before, during and immediately after the post election violence of the
year 2007/2008, a time when data could not be obtained and/or recorded. The original
time series data when analysed produced a SARIMA model (0,1,1)(2,0, 0h2 as the best
candidate for the lower segment. SARIMA (0,1,2)(0,0,1)12 was produced for the upper
segment using autoarima function in R package. The missing data were estimated using
forecast from the lower segment which was extended to the in sample forecast in the upper
segment. The regression test between predicted and the original values in upper segment
proves strong positive relationship indicating high level of accuracy on predictability of
the model used. | en_US |
