Statistical analysis and prediction of time series

Course unit code

В.2.2

Course unit title

Options

Name(s), surname(s) and title of lecturer(s)

Evgeny Pchelintsev, PhD, Associate Professor

Level of course

Master

Semester

3

ECTS credits

3

Working hours

Contact hours

Lectures

32

Total

108

Work placement

Laboratory works in Computer class

Prerequisites

It is assumed that the students have mastered the following disciplines «Mathematical Analysis», «Linear Algebra», «Probability Theory and Mathematical Statistics», «Differential Equations», «Stochastic processes», «Numerical Methods».

Language of instruction

English (Russian)

Objectives of the course

Learning outcomes

A student’s assessments methods

- Prepare master mathematicians to use basic models and methods of time series analysis,

- Give skills of work with time series, constructing on macroeconomic and financial data.

- Review the basic theoretical and practical aspects of modeling univariate and multivariate time series.

In mastering the subject of Time Series Analysis the student will acquire the following knowledge:

- The basic time series models.

- To be able to state a problem which could be solved in the framework of Time Series Analysis, to construct a stochastic model of the time series, to develop an algorithm for identifying unknown parameters of the dynamic system, to compose a program on a computer language of high level, to analyze the data obtained in numerical experiments.

The current control of mastering the discipline includes one written test and three reports on the labs.

The final control – exam.

Teaching methods

Lectures, Labs

List of Topics

Topic title

Contact hours

Assignments and independent study hours

Time Series and Stochastic Processes

4

Modeling of stationary time series

8

Lab 1

Time series models, including heteroscedasticity

6

Lab 2

Modeling of non-stationary time series

8

Lab 3

Time series with continuous time

6

Written test

Assessment requirements

In during the semester  40 points

Assessment criteria

Each lab 10 points and test 10 points

The composition of final accumulative mark

Exam 60 points.

Examination ticket consists of two theoretical questions (10x2=20) and two exercises (20x2=40).

Author of the course

Evgeny Pchelintsev