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Course Info

  • TIME SERIES ANALYSIS AND FORECASTING

TIME SERIES ANALYSIS AND FORECASTING

About this Course

This course aims to expose to the students advanced univariate and multivariate regression models for forecasting purposes using time series data. Using computer packages to develop the models using real data is emphasized. The students are expected to be able to develop forecast models and to present the results.

Course Syllabus

Syllabus Info
Introduction to time series
Forecasting Scenario
Definition of time series
Component of time series
Error measures
Within and out-of-sample model evaluation

Univariate modelling techniques
Concept and ideas
Naive forecast
Average methods
Exponential smoothing methods
Decomposition method

Single equation econometric modelling
Practical issues and problems
Multi-variables modelling procedure
Specification error and diagnostic testing procedures
Generate (conditional and unconditional) forecast value

Stochastic time series
Random walk process
Test for stationary
Forecasting under stationary and non-stationary condition

Box-Jenkins methodology
Autoregressive (AR) and Moving Average (MA) modelling
Autocorrelation (AC) and Partial Auto Correlation
Using Backward Shift operator
Model identification
ARMA/ARIMA modelling
Model selection and evaluation

Frequently Asked Questions

Course FAQ
Q1 : What is basic requirement for this course?
A1 : Basic statistics knowledge.