Date of Award

2006

Degree Type

Thesis

Degree Name

Master of Science (Mathematics and Planning)

School

School Of Mathematics and Engineering

Faculty

Computing, Health And Science

First Advisor

Associate Professor James Cross

Abstract

Time Series is a sequential set of data measured over time. Examples of time series arise in a variety of areas, ranging from engineering to economics. The analysis of time series data constitutes an important area of statistics. Since, the data are records taken through time, missing observations in time series data are very common. This occurs because an observation may not be made at a particular time owing to faulty equipment, lost records, or a mistake, which cannot be rectified until later. When one or more observations are missing it may be necessary to estimate the model and also to obtain estimates of the missing values. By including estimates of missing values, a better understanding of the nature of the data is possible with more accurate forecasting. Different series may require different strategies to estimate these missing values. It is necessary to use these strategies effectively in order to obtain the best possible estimates. The objective of this thesis is to examine and compare the effectiveness of various techniques for the estimation of missing values in time series data models.