Forecasting: Principles and Practice
Welcome
1
Getting started
1.1
What can be forecast?
1.2
Forecasting, planning and goals
1.3
Determining what to forecast
1.4
Forecasting data and methods
1.5
Some case studies
1.6
The basic steps in a forecasting task
1.7
The statistical forecasting perspective
1.8
Exercises
1.9
Further reading
2
Time series graphics
2.1
ts
objects
2.2
Time plots
2.3
Time series patterns
2.4
Seasonal plots
2.5
Seasonal subseries plots
2.6
Scatterplots
2.7
Lag plots
2.8
Autocorrelation
2.9
White noise
2.10
Exercises
2.11
Further reading
3
The forecaster’s toolbox
3.1
Some simple forecasting methods
3.2
Transformations and adjustments
3.3
Residual diagnostics
3.4
Evaluating forecast accuracy
3.5
Prediction intervals
3.6
The forecast package in R
3.7
Exercises
3.8
Further reading
4
Judgmental forecasts
4.1
Beware of limitations
4.2
Key principles
4.3
The Delphi method
4.4
Forecasting by analogy
4.5
Scenario Forecasting
4.6
New product forecasting
4.7
Judgmental adjustments
4.8
Further reading
5
Linear regression models
5.1
The linear model
5.2
Least squares estimation
5.3
Some useful predictors
5.4
Evaluating the regression model
5.5
Selecting predictors
5.6
Forecasting with regression
5.7
Matrix formulation
5.8
Nonlinear regression
5.9
Correlation, causation and forecasting
5.10
Exercises
5.11
Further reading
6
Time series decomposition
6.1
Time series components
6.2
Moving averages
6.3
Classical decomposition
6.4
X11 decomposition
6.5
SEATS decomposition
6.6
STL decomposition
6.7
Forecasting with decomposition
6.8
Exercises
6.9
Further reading
7
Exponential smoothing
7.1
Simple exponential smoothing
7.2
Trend methods
7.3
Holt-Winters’ seasonal method
7.4
A taxonomy of exponential smoothing methods
7.5
Innovations state space models for exponential smoothing
7.6
Estimation and model selection
7.7
Forecasting with ETS models
7.8
Exercises
7.9
Further reading
8
ARIMA models
8.1
Stationarity and differencing
8.2
Backshift notation
8.3
Autoregressive models
8.4
Moving average models
8.5
Non-seasonal ARIMA models
8.6
Estimation and order selection
8.7
ARIMA modelling in R
8.8
Forecasting
8.9
Seasonal ARIMA models
8.10
ARIMA vs ETS
8.11
Exercises
9
Dynamic regression models
9.1
Estimation
9.2
Regression with ARIMA errors in R
9.3
Forecasting
9.4
Stochastic and deterministic trends
9.5
Dynamic harmonic regression
9.6
Lagged predictors
9.7
Exercises
9.8
Further reading
10
Forecasting hierarchical or grouped time series
10.1
Hierarchical time series
10.2
Grouped time series
10.3
Base and coherent forecasts
10.4
The bottom-up approach
10.5
Top-down approaches
10.6
Middle-out approach
10.7
The projection matrix
10.8
The optimal reconciliation approach
10.9
Exercises
10.10
Further reading
11
Advanced forecasting methods
11.1
Complex seasonality
11.2
Forecasting counts
11.3
Vector autoregressions
11.4
Neural network models
11.5
Exercises
12
Some practical forecasting issues
12.1
Weekly, daily and sub-daily data
12.2
Time series of counts
12.3
Ensuring forecasts stay within limits
12.4
Forecast combinations
12.5
Prediction intervals for aggregates
12.6
Backcasting
12.7
Forecasting very short time series
12.8
Forecasting very long time series
12.9
One-step forecasts on test data
12.10
Multi-step forecasts on training data
12.11
Dealing with missing values and outliers
12.12
Bootstrapping and bagging
Using R
References
Published by OTexts with bookdown
Forecasting: Principles and Practice
11.2
Forecasting counts
croston
Poisson models?
Other intermittent demand models