Slides¶

Univariate Time Series Analysis
Univariate Time Series Analysis (Print Optimized)
In-person Slides (pdf)
In-person Slides (web)
In-person Slides (Jupyter Notebook)

Data Files

Time Series Data (pandas HDF) used in the IPython notebook-based slides.

Executing code in jupyter slides

See the instructions for installing the mfe package to install some function used in the slides.

Assignment¶

Due Friday 5th Week Hilary

Computational Assignment III
Demo Autograder (Jupyter Notebook)
Demo Autograder (Python)
Solutions Template

Weekly Assignment¶

Week 1 Assignment

Solutions

Week 1 Assignment Solution

Practice Quiz¶

Oxford MFE Students only (Canvas)

Week 1 Quiz

Pre-lecture content¶

Reading

The readings listed correspond to the pre-recorded content. You should either watch the pre-recorded content or read the notes prior to the Week 1 lecture.

Sections 4.1-4.4 (ex 4.4.1), 4.6, and 4.7 from the Time Series Notes

Presentation

Oxford MFE Students only (Canvas)

1. Introduction to Time Series Analysis
2. Stochastic Processes
3. Autocovariance
4. Stationarity
5. Ergodicity
6. White Noise
7. Linear Time Series Processes
8. Autoregressive-Moving Average Processes
9. Conditional Moments
10. Stationarity of AR Processes
11. Moments of an AR(1) Process
12. Autocorrelations Structure of ARMA Processes
13. Autocorrelation Structure of ARMA Processes
14. Estimating Autocorrelations and Partial Autocorrelations
15. Testing Autocorrelations and Partial Autocorrelations
16. Parameter Estimation
17. Model Building
18. Model Diagnostics