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2020/21 Undergraduate Module Catalogue

MATH3802 Time Series

10 creditsClass Size: 120

Module manager: Dr Haiyan Liu
Email: h.liu1@leeds.ac.uk

Taught: 1 Jan to 31 May, 1 Sep to 31 Dec (adv yr) View Timetable

Year running 2020/21

Pre-requisite qualifications

MATH2715 or MATH2735.

This module is mutually exclusive with

MATH5802MTime Series and Spectral Analysis

This module is not approved as a discovery module

Module summary

In time series, measurements are made at a succession of times, and it is the dependence between measurements taken at different times which is important. The module will concentrate on techniques for model identification, parameter estimation, diagnostic checking and forecasting within the autoregressive moving average family of models and their extensions.

Objectives

Objectives: To develop statistical techniques for the analysis of data collected sequentially through time.

On completion of this module, students should be able to:
a) assess graphically the stationarity of a time series, including the calculation and use of a sample autocorrelation on function;
b) evaluate the autocorrelation function and partial autocorrelation function for AR, MA and ARMA modes;
c) use the autocorrelation and partial autocorrelation functions and other diagnostics to formulate, test and modify suitable hypotheses about time series models;
d) forecast future values of a time series;
e) use a statistical package with real data to facilitate the analysis of the time series data and write a report giving and interpreting the results.

Syllabus

1. Overview. Stationarity, outline of Box-Jenkins approach through identification of model, fitting, diagnostic checking, and forecasting. Mean, autocorrelation function, partial autocorrelation function.
2. Models. Autoregressive (AR) models, moving average (MA) models, ARMA models, their autocorrelation functions, and partial autocorrelation functions. Transformations and differencing to achieve stationarity, ARIMA models.
3. Estimation and diagnostics. Identifying possible models using autocorrelation function, and partial autocorrelation function. Estimation, outline of maximum likelihood, conditional and unconditional least squares approaches. Diagnostic checking, methods and suggestions of possible model modification.
4. Forecasting. Minimum mean square error forecast and forecast error variance, confidence intervals for forecasts, updating forecasts, other forecasting procedures.
5. Seasonality, time series regression.

Teaching methods

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

Delivery typeNumberLength hoursStudent hours
Lecture111.0011.00
Practical12.002.00
Private study hours87.00
Total Contact hours13.00
Total hours (100hr per 10 credits)100.00

Private study

Studying and revising of course material.
Completing of assignments and assessments.

Opportunities for Formative Feedback

Regular problem solving assignments

Methods of assessment

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information


Coursework
Assessment typeNotes% of formal assessment
In-course Assessment.20.00
Total percentage (Assessment Coursework)20.00

There is no resit available for the coursework component of this module. If the module is failed, the coursework mark will be carried forward and added to the resit exam mark with the same weighting as listed above.


Exams
Exam typeExam duration% of formal assessment
Open Book exam2 hr 00 mins80.00
Total percentage (Assessment Exams)80.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 10/08/2020 08:42:07

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