Fall 2019 - STAT 685 G100
Applied Time Series Analysis (3)
Class Number: 4602
Delivery Method: In Person
Course Times + Location:
Sep 3 – Dec 2, 2019: Mon, 5:30–6:20 p.m.
Sep 3 – Dec 2, 2019: Thu, 4:30–6:20 p.m.
Exam Times + Location:
Dec 6, 2019
Fri, 7:00–10:00 p.m.
Prerequisites:STAT 302 or STAT 305 or STAT 650 or BUEC 333 or permission of instructor. Open only to graduate students in departments other than Statistics & Actuarial Science.
Introduction to linear time series analysis including moving average, autoregressive and ARIMA models, estimation, data analysis, forecasting errors and confidence intervals, conditional and unconditional models, and seasonal models.
- Autocorrelation, seasonality, and trends in time series and their impacts on standard statistical inference techniques.
- Autoregressive models: definition, model formulation, and data analysis
- Moving average models: definition model formulation, and data analysis
- ARIMA models: definition, model formulation, and data analysis
- Introduction to forecasting with linear time series models
- Introduction to nonparametric fitting of trends and cycles to time series data
- Assignments 20%
- Midterm 30%
- Final 50%
Above grading is subject to change.
Time Series Analysis with Applications in R (2nd ed.) by Jonathan D. Cryer and Kung-Sik Chan. Publisher: Springer
- Book is available on-line for free through the SFU Libarary
Hardcover ISBN: 978-0-387-75958-6
Softcover ISBN: 978-1-4419-2613-5
Graduate Studies Notes:
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