Time Series Analysis in R
Preface
1
Toolbox
1.1
R Structures
ts, zoo, and xts
tsibble
1.2
Fitting Models
1.3
Evaluating Models
1.4
Evaluating Accuracy
2
Exploratory Analysis
2.1
Graphical Analysis
2.2
Transformations
2.3
Decomposition
2.3.1
Classical Decomposition
2.3.2
X-11 and SEATS
2.3.3
STL
3
Time Series Regression
3.1
Exploratory Analysis
3.2
Fit Model
Special Predictors
3.3
Model Evaluation
Outliers, Leverage Points, and Influential Points
3.4
Variable Selection
3.5
Predicting Values
3.6
Nonlinear Regression
4
Exponential Smoothing
4.1
Simple Exponential Smoothing (SES)
4.2
Holt’s Linear Method
4.3
Additive Damped Trend Method
4.4
Holt-Winters
4.4.1
Additive Holt-Winters Method
4.4.2
Multiplicative Holt-Winters Method
4.5
Model Selection with CV
4.6
Model Selection via Maximum Likelihood
5
ARIMA
5.1
Tranformations and Differencing
5.2
Autoregressive Models
5.3
Moving Average Models
5.4
Non-Seasonal ARIMA
6
Dynamic Harmonic Regression
6.1
TBATS Model
References
Published with bookdown
Time Series Analysis
References
Rob J Hyndman, George Athanasopoulos. 2021.
Forecasting: Principles and Practice
. 3rd ed. Otexts.
https://otexts.com/fpp3/
.