Time Series Analysis in R
Preface
1
Basics
1.1
Common Frameworks
1.2
Fitting Models
1.3
Evaluating Fit
1.4
Model Selection
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 (ETS)
4.1
Simple Exponential Smoothing (SES)
4.2
Holt Linear
4.3
Additive Damped Trend
4.4
Holt-Winters
4.5
Auto-fitting
5
ARIMA
5.1
Stationary Time Series
5.2
Autoregressive: AR(
p
)
5.3
Moving Average: MA(
q
)
5.4
Non-Seasonal: ARIMA(
p
,
d
,
q
)
5.5
Seasonal: ARIMA(
p
,
d
,
q
)(
P
,
D
,
Q
)m
5.6
Fitting an ARIMA Model
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/
.