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STAT 510 Applied Time Series Analysis

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Lesson

Lesson 1: Time Series Basics

Lesson 2: MA Models, Partial Autocorrelation, Notational Conventions

Lesson 3: Identifying and Estimating ARIMA models; Using ARIMA models to forecast future values

Lesson 4: Seasonal Models
- 4.1 Seasonal ARIMA models
- 4.2 Identifying Seasonal Models and R Code

Lesson 5: Smoothing and Decomposition Methods and More Practice with ARIMA models
- 5.1 Decomposition Models
- 5.2 Smoothing Time Series

Lesson 6: The Periodogram
- 6.1 The Periodogram

Lesson 8: Regression with ARIMA errors, Cross correlation functions, and Relationships between 2 Time Series
- 8.1 Linear Regression Models with Autoregressive Errors
- 8.2 Cross Correlation Functions and Lagged Regressions

Lesson 9: Prewhitening; Intervention Analysis
- 9.1 Pre-whitening as an Aid to Interpreting the CCF
- 9.2 Intervention Analysis

Lesson 10: Longitudinal Analysis/ Repeated Measures
- 10.1 Repeated Measures and Longitudinal Data

Lesson 11: Vector Autoregressive Models/ ARCH Models
- 11.1 ARCH/GARCH Models
- 11.2 Vector Autoregressive models VAR(p) models

Lesson 12: Spectral Analysis
- 12.1 Estimating the Spectral Density

Lesson 13: Fractional Differencing and Threshold Models
- 13.1 Long Memory Models and Fractional Differences
- 13.2 Threshold Models

Lesson 14: Review
- 14.1 Course Summary

Resources

Introduction to R
Learning Online Orientation

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