CONCORDIA UNIVERSITY 
STAT 460: Time Series and Forecasting 
* Comprehensive Study and Exam Preparation Guide 2024 
 
Part 01. Revision Notes 
Part 02. Practice Examination Paper 
Part 03. Detailed Solutions & Explanations
Part 04. Proven Exam Strategies & Tips 
 
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Concordia University - STAT 460: Time Series and Forecasting

C$35.00

ACADEMIC YEAR 2023 - 2024

University	Concordia University
Study Level	Undergraduate
Course Code	STAT 460
Course Name	Time Series and Forecasting
Notes Title	Comprehensive Study and Exam Prep. Guide
Format	PDF
Page Length	69

* This comprehensive study guide is designed for students enrolled in STAT 460: Time Series and Forecasting at Concordia University. It covers all assessments throughout the course extensively. Whether you're preparing for the crucial 2024 exam or reinforcing your understanding, this guide offers essential exam techniques and focuses on core questions relevant to your academic success. Featuring clear diagrams to enhance comprehension and retention, this self-study resource is updated to reflect the latest course content, ensuring you're fully prepared for all academic evaluations.

STAT 460: Time Series and Forecasting at Concordia University explores the properties and processes of time series models. Topics covered include forecasting techniques such as trend and irregular component analysis using methods like multiple regression analysis and exponential smoothing. The course also delves into forecasting seasonal time series using additive and multiplicative decomposition methods, as well as the Box-Jenkins methodology, moving average, autoregressive, and mixed models. Prerequisite for this course is STAT 360.

On completion of this course, you will be expected to be able to:

Understand the objectives and examples of time series analysis.
Learn basic time series models, including ARMA (p, q) processes.
Explore techniques for estimating and removing trends and seasonal components.
Analyze autocorrelation functions (ACF) and partial autocorrelation functions (PACF) for model identification.
Forecast stationary time series using ARMA models.
Study spectral densities and periodograms for spectral analysis.
Perform preliminary and maximum likelihood estimation for ARMA processes.
Conduct diagnostic checking and select appropriate model orders.
Apply time-invariant linear filters and analyze the spectral density of ARMA processes.

COURSE CODE	COURSE NAME
ACCO 310	Financial Reporting I
ACCO 320	Financial Reporting II
ACCO 340	Income Taxation in Canada
COMM 215	Business Statistics
COMM 217	Financial Accounting
COMM 222	Organizational Behaviour and Theory
COMM 223	Marketing Management I
COMM 305	Managerial Accounting
COMM 308	Introduction to Finance
COMM 315	Business Law and Ethics
ECON 201	Introduction to Microeconomics
ECON 203	Introduction to Macroeconomics
ECON 221	Statistical Methods I
ECON 222	Statistical Methods II
ECON 301	Intermediate Microeconomic Theory I
ECON 421	Econometrics I
ECON 423	Topics in Applied Econometrics
FINA 385	Theory of Finance I
FINA 395	Theory of Finance II
FINA 411	Portfolio Management
FINA 412	Options and Futures

Concordia University - STAT 460: Time Series and Forecasting

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