Simon Fraser University's STAT 485: Applied Time Series Analysis course offers an in-depth introduction to linear time series analysis, covering moving average, autoregressive, and ARIMA models. It includes topics such as estimation, data analysis, forecasting errors, confidence intervals, conditional and unconditional models, and seasonal models. Prerequisites are STAT 260 and one of STAT 285, STAT 302, STAT 305, or ECON 333, with a minimum grade of C-. Students with credit for ECON 484 are not eligible. This is a quantitative course.

By the end of this course, you should be able to:

• Understand the properties of moving average models.

• Learn how to estimate parameters in moving average models.

• Develop skills to make predictions using moving average models.

• Comprehend the properties of autoregressive models.

• Gain knowledge in estimating parameters for autoregressive models.

• Acquire techniques for making predictions with autoregressive models.

• Explore the combined properties of ARMA models.

• Learn to estimate parameters and predict using ARMA models.

• Understand and apply seasonal models for time series data.

COURSE CODE

COURSE NAME

BUS 232

Data and Decisions I

BUS 233

Introduction to Business Law and Ethics

BUS 251 

Financial Accounting I

BUS 272 

Behaviour in Organizations 

BUS 312

Introduction to Finance

BUS 315

Investments

BUS 316 

Derivative Securities

BUS 320 

Financial Accounting: Assets

BUS 321 

Financial Accounting: Equity

BUS 322 

Intermediate Managerial Accounting

BUS 341 

Fundamentals of Marketing

BUS 410 

Financial Institutions

BUS 413 

Corporate Finance

ECON 103 

Principles of Microeconomics

ECON 105 

Principles of Macroeconomics

ECON 201 

Microeconomic Theory I: Competitive Behavior

ECON 233 

Introduction to Economic Data and Statistics

ECON 333 

Statistical Analysis of Economic Data

STAT 270 

Introduction to Probability and Statistics

STAT 485 

Applied Time Series Analysis