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    Multivariate and Time Series Analysis
    STAT S802F
      More information: Course Guide
    Multivariate and Time Series Analysis
    Course Start Date
    Aut 2019
    Course Level
    Postgraduate
    Length in Terms
    2 terms
    Credits
    10
    Language
    English
    Fees ($) (including lab fees)
    Future Terms
    Aut 2020
    Quota and Schedule
    Course
    Start Date
    Course Level Length in Terms Credits
    Language
    Fees ($)
    (including lab fees)
    Future Terms
    Aut 2019
    Postgraduate 2 terms 10
    English
    Aut 2020

    Course Coordinator: Dr C F Chu , BEng, MPhil, PhD (CUHK)

    Important notes
    This course will be taught through a part-time face-to-face mode. Lectures and tutorials will be scheduled on weekday evenings, Saturdays or Sundays. This is the core course for the Postgraduate Certificate in Quantitative Analysis programme.

    The course begins by discussing the regression model and then focuses on distinguishing between different types of time series. The course then continues with in-depth discussion of the procedures required to perform different forecasting techniques. The course concludes with a discussion of statistical methods for analysing multivariate data through examples in various fields of application and hands-on experience with the statistical software.

    Advisory prerequisite(s)
    You are advised to have some background knowledge in statistics or related disciplines.

    Aims
    This course aims to:

    • Develop in students an understanding of the basic concepts and techniques of time series and forecasting methods for data analysis in social, economic, and scientific phenomena;

    • Provide students with a thorough understanding of multivariate data analysis through the solving of practical problems in the fields of business and finance and hands-on experience with the statistical software.

    Contents
    The course will cover the following topics:

    • Statistical methods and models

    • Regression model and its application in forecasting

    • Seasonal and non-seasonal time series models

    • ARIMA time series models

    • Seasonal autoregressive models

    • Describing and displaying multivariate data

    • Multivariate Normal distribution and its sampling theory

    • Tests of hypotheses on means and covariance matrices

    • Multivariate methods and classification analysis

    Learning support
    There will be regular face-to-face lectures and tutorials throughout the course.

    Course assessment
    Course assessment will be divided into a continuous assessment portion and a final examination. The continuous assessment portion will include four assignments (from which the best three scores will be used to determine the final grade). A three-hour final examination will be conducted at the end of the course. Students are required to submit assignments via the Online Learning Environment (OLE).

    Online requirement
    This course is supported by the Online Learning Environment (OLE). You can find the latest course information from the OLE. Through the OLE, you can communicate electronically with your lecturer as well as other students. To access the OLE, you will need to have access to the Internet. The use of the OLE is required for the study of this course.

    Set book(s)
    To be announced.

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