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ETC3550/ETC5550 Applied Forecasting 2020
发布时间:2022-06-01
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Semester One 2020
Examination Period
ETC3550/ETC5550
Applied Forecasting
SECTION A
Write about a quarter of a page each on any four of the following topics. (Clearly state if you agree or disagree with each statement. No marks will be given without any justification.)
1. The disadvantage of using a test set for choosing a forecasting model is that it uses only a small proportion of the data.
2. The best forecasting models adapt rapidly to changes in the trend and seasonal patterns.
3. With STL decompositions and ETS models, we always need to transform our data before estimating the components.
4. The mean of a stationary AR(3) process
pt = c + 61pt-1 + 62pt-2 + 63pt-3 + et,
where et ~ NID(0, g2 ), is equal to c.
(You can write out your answer elsewhere and upload it as an image if you prefer)
5. ARIMA models are better than ETS models because there are more possible models available.
6. Regression models are not very useful for forecasting because you have to forecast all the predictors as well.
SECTION B
Figures 1, 2and 3relate to the number of students (in thousands) arriving in Australia from China over the period January 2005 – February 2020.
1. Using Figures 1, 2and 3, describe the student arrivals from China. Carefully comment on the interesting features of all three plots.
60
40
20
0 |
2005 Jan 2010 Jan 2015 Jan 2020 Jan Month [1M] |
Figure 1:
2. Using the code below, describe what is plotted in all panels of Figures 4and 5. Comment on the default settings for window in trend() and season(), and the efect of robust=TRUE. Which settings would you consider appropriate in this case?
ch_edu_arrivals %>%
model(STL (log(Count))) %>% components() %>%
autoplot() + ggtitle("STL decomposition")
‘log(Count)‘ = trend + season_year + remainder
4 3 2 1 0 2.5 2.0 1.5 1.0 1 0 −1 0.0 −0.5 −1.0 |
2005 Jan 2010 Jan 2015 Jan 2020 Jan Month Figure 4: |
4 3 2 1 0 2.5 2.0 1.5 1.0 1 0 −1 0.5 0.0 −0.5 −1.0 −1.5 |
ch_edu_arrivals %>% model(STL (log(Count), robust = TRUE )) %>% components() %>% autoplot() + ggtitle("Robust STL decomposition") ‘log(Count)‘ = trend + season_year + remainder
2005 Jan 2010 Jan 2015 Jan 2020 Jan Month
|
3. You have been asked to provide forecasts for the next three years for the Chinese student arrivals series assuming that the travel bans will be lifted soon and travel will commence as normal in July 2020.
Consider applying each of the methods and models below. Comment, in a few words each, on whether each one is appropriate for forecasting the data. No marks will be given for simply guessing whether a method or a model is appropriate without justifying your choice.
Start your response by stating: suitable or not suitable.
Seasonal naïve method.
(b) An STL decomposition combined with the drift method to forecast the seasonally adjusted component.
(c) An STL decomposition on the log transformed data combined with an ETS to forecast the seasonally adjusted component.
(d) Holt-Winters method with damped trend and multiplicative seasonality.
(e) ETS(A,A,A).
(f) ETS(M,A,M).
(g) ARIMA(1,12,4).
(h) ARIMA(3,2,1)(1,1,0)12 on the log transformed data.
(i) ARIMA(3,1,1)(2,1,0)12 on the log transformed data.
(j) Regression model with time and Fourier terms.