Forecasting: Principles and Practice Book Club
Welcome
Book club meetings
Pace
1
Getting started
1.1
SLIDE 1
1.2
Meeting Videos
1.2.1
Cohort 1
1.2.2
Cohort 2
2
Time series graphics
2.1
SLIDE 1
2.2
Meeting Videos
2.2.1
Cohort 1
2.2.2
Cohort 2
3
Time series decomposition
3.1
Transformations and adjustments
3.2
Time series components
3.2.1
Additive decomposition
3.2.2
Multiplicative decomposition
3.2.3
Example: Employment in the US retail sector
3.3
Classical decompositions
3.3.1
Moving average smoothing
3.3.2
Moving averages of moving averages
3.3.3
Weighted moving averages
3.3.4
Additive decomposition
3.3.5
Multiplicative decomposition
3.4
Methods used by official statistics agencies
3.4.1
X-11 method
3.4.2
The SEATS method
3.5
Exercises
3.5.1
Exercise 5
3.5.2
Exrecise 10
3.5.3
Exrecise 10
3.6
Further reading
3.7
Meeting Videos
3.7.1
Cohort 1
3.7.2
Cohort 2
4
Time series features
4.1
Some simple statistics
4.1.1
Five summary statistics
4.2
ACF features
4.3
STL features
4.4
Other features
4.5
Exploring Australian tourism data
4.5.1
Principal Component Analysis (PCA)
4.6
Meeting Videos
4.6.1
Cohort 1
4.6.2
Cohort 2
5
The forecaster’s toolbox
5.1
EXRCISE 1
5.1.1
Australian Population (global_economy)
5.1.2
Bricks (aus_production)
5.1.3
NSW Lambs (aus_livestock)
5.1.4
Household wealth (hh_budget)
5.1.5
Australian takeaway food turnover (aus_retail)
5.2
Meeting Videos
5.2.1
Cohort 1
5.2.2
Cohort 2
6
Judgmental forecasts
6.1
SLIDE 1
7
Time series regression models
7.1
Linear Regression Model
7.2
US consumption expenditure
7.3
Multiple Linear Regression Model
7.4
Least squares estimation
7.4.1
Example
7.5
Exercises
7.6
Meeting Videos
7.6.1
Cohort 2
8
Exponential smoothing
8.1
Simple Exponential smoothing
8.2
Methods
8.2.1
Trend
8.3
Exercises
8.3.1
Exercise 5
8.4
Meeting Videos
8.4.1
Cohort 2
9
ARIMA models
9.1
Exercise 7
9.1.1
a. Use ARIMA() to find an appropriate ARIMA model. What model was selected.
9.1.2
b. Write the model in terms of the backshift operator.
9.1.3
c. Plot forecasts from an ARIMA(0,1,0) model with drift and compare these to part a.
9.1.4
d. Plot forecasts from an ARIMA(2,1,2) model with drift and compare these to parts a. and c.
9.1.5
e. Plot forecasts from an ARIMA(0,2,1) model with a constant. What happens?
9.2
Exercise 11
9.2.1
Do the data need transforming?
9.2.2
Are the data stationary?
9.2.3
Which of the models is the best according to their AIC values?
9.2.4
Do the residuals resemble white noise?
9.2.5
Compare the forecasts obtained using ETS()
9.3
Meeting Videos
9.3.1
Cohort 2
10
Dynamic regression models
10.1
White Noise and Autocorrelation
10.2
What is the Difference Between ARIMA and ARMA Model?
10.3
Example: US Personal Consumption and Income
10.4
Forecast
10.5
Difference between Stochastic and deterministic trends
10.6
Dynamics
10.7
Impact of a predictor
10.8
Forecast with different levels of TVadverts
10.9
Meeting Videos
10.9.1
Cohort 2
11
Forecasting hierarchical and grouped time series
11.1
SLIDE 1
11.2
Meeting Videos
11.2.1
Cohort 2
12
Advanced forecasting methods
12.1
Complex seasonality
12.1.1
Case Study 1
12.1.2
Case Study 2
12.2
Prophet model
12.2.1
Case Study 3
12.2.2
Case Study 4
12.3
Vector autoregressions (VAR)
12.3.1
Case Study 5
12.4
Neural network models
12.4.1
Case Study 6
12.5
Bootstrapping and bagging
12.5.1
Case Study 7
12.5.2
Bagging = bootstrap aggregating
12.6
Exercises
12.7
Meeting Videos
12.7.1
Cohort 2
13
Some practical forecasting issues
13.1
SLIDE 1
13.2
Meeting Videos
13.2.1
Cohort 2
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Forecasting: Principles and Practice Book Club
Chapter 5
The forecaster’s toolbox
Learning objectives:
Learn how to apply NAIVE(y), SNAIVE(y) or RW(y ~ drift()) appropriately