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General
Information A
Time Series is a collection of observations, usually taken in time. For example:
values of consumption in the UK over several years, unemployment percentage
in the UK over several years, surface air temperature change for the globe,
arrival phases of an earthquake, IBM common stock closing prices, chemical
process temperature readings. As in many data, here too, some uncertainty of
observations may be present. If it can be considered as random, then the time
series is viewed as a random variable. A statistical analysis of the past
data can reveal various features of the phenomenon of interest, for example
whether there is any trend (i.e., increasing unemployment), or whether the
series follows seasons (i.e., smaller unemployment in July). Also, what is
very important, the analysis allows for prediction with some confidence of the
future state of the phenomenon, so called forecasting. This is because the
data are often highly dependent (i.e., unemployment this month depends on the
level of unemployment last month). Hence, correlation of the random variables
is one of the main features of the time series analysis. This course
introduces some of the descriptive methods and theoretical techniques that
are used to analyze time series. Many examples will be shown from various
applications, such as business and economics, climatology, chemistry,
biology. Real data sets will be analyzed using the statistical computing
package MINITAB. 
Computing Time
series data are often very large and any sensible analysis requires fast
calculations. There are various statistical packages which have many time
series analysis options built in. Alternatively one has to write special
computer programs. In this course we will use MINITAB and its quite wide set
of functions available for time series description and analysis. You need to
have an account on the PC network. While logging in remember to choose Time
Series from the list of courses. This will allow you to get the data for
analysis. Knowledge of MINITAB is not essential (of course it would be
helpful). There will be an Introduction to MINITAB session in a computer lab
at the beginning of the course. 
Prerequisites An
essential prerequisite is Probability II. Good understanding of bivariate
random variables is important to follow the course. Although the course is very
much selfcontained Calculus II and Fundamentals of Statistics II are advised
to be passed. Differentiation skills will be needed as well as understanding
the notion of estimation. Students not having passed these two courses may
take the Time Series course but should be aware of a necessity to read up on
these topics. 
Assessment 20%
interm, 80% exam paper. There
will be six computer labs (every other week, starting in week 2). Attendance to
the labs is compulsory. The
coursework will be discussed either during the labs or during lectures and solutions
will be put on the course website. However, the coursework will not be
marked. Instead, there will be two (multiple choice) tests: one in week 7 and
one in week 12 of the term. The tests will be based on the material very
similar to the coursework. Each test counts 10% of the final mark. 
Updated on 18
September 2008 
