Week 1: Introductions to time series analysis and the AR(1) process

Time Series Analysis

The AR(1) process, Stationarity, ACF, PACF, Differencing, and Smoothing
Coursera
notes
Bayesian Statistics
Autoregressive Models
Time Series
Author

Oren Bochman

Published

Sunday, October 27, 2024

Keywords

time series, strong stationarity, weak stationarity, autocorrelation function, ACF, partial autocorrelation function, PACF, smoothing, trend, seasonality, Durbin-Levinson recursion, Yule-Walker equations, differencing operator, back shift operator, moving average, AR(1) process, R code

Reuse

CC SA BY-NC-ND

Citation

BibTeX citation:
@online{bochman2024,
  author = {Bochman, Oren},
  title = {Week 1: {Introductions} to Time Series Analysis and the
    {AR(1)} Process},
  date = {2024-10-27},
  url = {https://orenbochman.github.io/notes/bayesian-ts/module5.html},
  langid = {en}
}
For attribution, please cite this work as:
Bochman, Oren. 2024. “Week 1: Introductions to Time Series Analysis and the AR(1) Process.” October 27, 2024. https://orenbochman.github.io/notes/bayesian-ts/module5.html.