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Tutor: Kelsa Duan
Date: 2024 S1
UNSW ECON2209
Business Forecasting
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ECON2209 Business Forecasting
Week 1 Lecture & Tutorial 精讲班
Problem 1: fpp3 1.8, Ex 1 (i.e. exercise 1 in section 1.8 of the textbook)
For case 4 in Section 1.5 of the textbook (forecasting weekly air passenger traffic), list the
possible predictor variables that might be useful, assuming that the relevant data are available.
Case 4
In this project, we needed to develop a model for forecasting weekly air passenger traffic on
major domestic routes for one of Australia’s leading airlines. The company required forecasts of
passenger numbers for each major domestic route and for each class of passenger (economy
class, business class and first class). The company provided weekly traffic data from the
previous six years.
Air passenger numbers are affected by school holidays, major sporting events, advertising
campaigns, competition behaviour, etc.
School holidays often do not coincide一致 in different Australian cities. school holidays
Sporting events sometimes move from one city to another. sporting events
During the period of the historical data, there was a major pilots’ strike罢工 during which there
was no traffic for several months. A new cut-price airline also launched 成立 and folded 倒闭.
competition behaviour
Towards the end of the historical data, the airline had trialled 尝试 a redistribution of some
economy class seats to business class, and some business class seats to first class. After
several months, however, the seat classifications reverted 恢复 to the original distribution.
advertising campaigns
相关知识点:
response variables: variables of interest in an experiment are called response or dependent
variables. 实验中你所关心的结果
predictor variables: variables in the experiment that affect the response and can be set or
measured by the experimenter are called predictor, explanatory, or independent variables. 会影
响实验结果的因素
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Problem 2: fpp3 1.8, Ex 2
For case 3 in Section 1.5 (car fleet company), describe the five steps of forecasting in the
context of this project. 要将理论和 case结合在一起写
Case 3
A large car fleet company asked us to help them forecast vehicle resale values. They purchase
new vehicles, lease them out for three years, and then sell them. Better forecasts of vehicle sales
values would mean better control of profits; understanding what affects resale values may allow
目的 leasing and sales policies to be developed in order to maximise profits.
At the time, the resale values were being forecast by a group of specialists. Unfortunately, they
saw any statistical model as a threat to their jobs, and were uncooperative in providing
information. Nevertheless, the company provided a large amount of data on response variables
previous vehicles and their predictor variables eventual resale values.
相关知识点：
fpp3 1.6 The basic steps in a forecasting task
A forecasting task usually involves five basic steps:
Step 1: Problem definition
Often this is the most difficult part of forecasting. Defining the problem carefully requires an
understanding of the way the forecasts will be used, who requires the forecasts, and how the
forecasting function fits within the organisation requiring the forecasts. A forecaster needs to
spend time talking to everyone who will be involved in collecting data, maintaining databases,
and using the forecasts for future planning.
Step 2: Gathering information
There are always at least two kinds of information required: (a) statistical data, and (b) the
accumulated expertise of the people who collect the data and use the forecasts. Often, it will be
difficult to obtain enough historical data to be able to fit a good statistical model. In that case,
the judgmental forecasting methods of Chapter 6 can be used. Occasionally, old data will be
less useful due to structural changes in the system being forecast; then we may choose to use
only the most recent data. However, remember that good statistical models will handle
evolutionary changes in the system; don’t throw away good data unnecessarily.
Step 3: Preliminary (exploratory) analysis
Always start by graphing the data. Are there consistent patterns? Is there a significant trend? Is
seasonality important? Is there evidence of the presence of business cycles? Are there any
outliers in the data that need to be explained by those with expert knowledge? How strong are
the relationships among the variables available for analysis? Various tools have been developed
to help with this analysis. These are discussed in Chapters 2 and 3.
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Step 4: Choosing and fitting models
The best model to use depends on the availability of historical data, the strength of relationships
between the forecast variable and any explanatory variables, and the way in which the forecasts
are to be used.
It is common to compare two or three potential models. Each model is itself an artificial
construct that is based on a set of assumptions (explicit and implicit) and usually involves one or
more parameters which must be estimated using the known historical data. We will discuss
regression models (Chapter 7), exponential smoothing methods (Chapter 8), Box-Jenkins
ARIMA models (Chapter 9), Dynamic regression models (Chapter 10), Hierarchical forecasting
(Chapter 11), and several advanced methods including neural networks and vector
autoregression (Chapter 12).
Step 5: Using and evaluating a forecasting model
Once a model has been selected and its parameters estimated, the model is used to make
forecasts. The performance of the model can only be properly evaluated after the data for the
forecast period have become available. A number of methods have been developed to help in
assessing the accuracy of forecasts. There are also organisational issues in using and acting on
the forecasts. A brief discussion of some of these issues is given in Chapter 5. When using a
forecasting model in practice, numerous practical issues arise such as how to handle missing
values and outliers, or how to deal with short time series. These are discussed in Chapter 13.