Data 624 Homework 5
Krutika Patel
2023-03-02
library(fpp3)8.1
###Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset.
vic_p <- aus_livestock %>% filter(Animal=='Pigs', State=='Victoria')
vic_p %>% autoplot()
Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the optimal values of α and ℓ0, and generate forecasts for the next four months.
vic_p_mod <- vic_p %>% model(ETS(Count ~ error("A") + trend("N") + season("N")))
vic_p_fc <- vic_p_mod %>% forecast(h = 4)
autoplot(vic_p) + autolayer(vic_p_fc)
report(vic_p_mod)## Series: Count
## Model: ETS(A,N,N)
## Smoothing parameters:
## alpha = 0.3221247
##
## Initial states:
## l[0]
## 100646.6
##
## sigma^2: 87480760
##
## AIC AICc BIC
## 13737.10 13737.14 13750.07The optimal values for α and ℓ0 respectively are 0.32 and 100646.6.
vic_p_fc$Count## <distribution[4]>
## [1] N(95187, 8.7e+07) N(95187, 9.7e+07) N(95187, 1.1e+08) N(95187, 1.1e+08)Compute a 95% prediction interval for the first forecast using ^y ±1.96s where s is the standard deviation of the residuals. Compare your interval with the interval produced by R.
s <- sd(residuals(vic_p_mod)$.resid)
mean <- vic_p_fc$.mean[1]
c(mean - (1.96*s),mean + (1.96*s))## [1] 76871.01 113502.10The 95% interval is (76871.01, 113502.10). This is smaller than the interval produced by R.
8.5
###Data set global_economy contains the annual Exports from many countries. Select one country to analyse.
Plot the Exports series and discuss the main features of the data.
ge <- global_economy %>% filter(Country == 'Peru')
ge %>% autoplot(Exports)
Use an ETS(A,N,N) model to forecast the series, and plot the forecasts.
ge_mod <- ge %>% model(ETS(Exports ~ error("A") + trend("N") + season("N")))
ge_fc <- ge_mod %>% forecast(h = 5)
ge_fc %>% autoplot(global_economy)
Compute the RMSE values for the training data.
accuracy(ge_mod)$RMSE## [1] 2.862248Compare the results to those from an ETS(A,A,N) model. (Remember that the trended model is using one more parameter than the simpler model.) Discuss the merits of the two forecasting methods for this data set.
ge_mod2 <- ge %>% model(ETS(Exports ~ error("A") + trend("A") + season("N")))
ge_fc2 <- ge_mod2 %>% forecast(h = 5)
ge_fc2 %>% autoplot(global_economy)
accuracy(ge_mod2)$RMSE## [1] 2.862429The RMSE for the ETS(A,N,N) model is slightly lower than that of the ETS(A,A,N) model, meaning it forecasts a little better.
Compare the forecasts from both methods. Which do you think is best?
The ETS(A,A,N) model forecasts an upwards trend while the ETS(A,N,N) models shows a flat line trend. The difference in RMSE between both models is very small. Taking that into account the ETS(A,A,N) model would be better suited based on its forecasts.
Calculate a 95% prediction interval for the first forecast for each model, using the RMSE values and assuming normal errors. Compare your intervals with those produced using R.
s1 <- sd(residuals(ge_mod)$.resid)
mean1 <- ge_fc$.mean[1]
c(mean1 - (1.96*s1),mean1 + (1.96*s1))## [1] 18.60605 29.92066The 95% interval is (18.60605, 29.92066).
ge_fc2$Exports## <distribution[5]>
## [1] N(24, 8.8) N(24, 18) N(24, 26) N(25, 35) N(25, 44)s2 <- sd(residuals(ge_mod2)$.resid)
mean2 <- ge_fc2$.mean[1]
c(mean2 - (1.96*s2),mean2 + (1.96*s2))## [1] 18.66408 29.98280The 95% interval is (18.66408, 29.98280)
ge_fc2$Exports[1]## <distribution[1]>
## [1] N(24, 8.8)8.6
Forecast the Chinese GDP from the global_economy data set using an ETS model. Experiment with the various options in the ETS() function to see how much the forecasts change with damped trend, or with a Box-Cox transformation. Try to develop an intuition of what each is doing to the forecasts.
[Hint: use a relatively large value of h when forecasting, so you can clearly see the differences between the various options when plotting the forecasts.]
c <- global_economy %>% filter(Country == 'China')
lam <- c %>% features(GDP,features=guerrero) %>% pull(lambda_guerrero)
c_mod <- c %>% model('SES' = ETS(GDP ~ error("A") + trend("N") + season("N")),
'Holt' = ETS(GDP ~ error("A") + trend("A") + season("N")),
'Damped Holt' = ETS(GDP ~ error("A") + trend("Ad", phi = 0.75) + season("N")),
'Box-Cox' = ETS(box_cox(GDP, lam) ~ error("A") + trend("A") + season("N")),
'Damped Box-Cox' = ETS(box_cox(GDP, lam) ~ error("A") + trend("Ad", phi = 0.75) + season("N")))
c_fc <- c_mod %>% forecast(h = 25)
c_fc %>% autoplot(global_economy, level = NULL)
8.7
###Find an ETS model for the Gas data from aus_production and forecast the next few years. Why is multiplicative seasonality necessary here? Experiment with making the trend damped. Does it improve the forecasts?
gas_mod <- aus_production %>% model('Multiplicative' = ETS(Gas ~ error("M") + trend("A") + season("M")),
'Damped Multiplicative' = ETS(Gas ~ error("M") + trend("Ad", phi = 0.75) + season("M")))
gas_fc <- gas_mod %>% forecast(h=20)
gas_fc %>% autoplot(aus_production, level = NULL)
Multiplicative seasonality is necessary due to the proportional increase
of the size of the seasonal cycle with the time series.
accuracy(gas_mod) %>% select('.model', 'RMSE')## # A tibble: 2 x 2
## .model RMSE
## <chr> <dbl>
## 1 Multiplicative 4.60
## 2 Damped Multiplicative 4.54The RMSE for the damped multiplicative model is lower than the RMSE for the multiplicative model. Making the trend does improve the forecasts.
8.8
Recall your retail time series data (from Exercise 8 in Section 2.10).
set.seed(246810)
myseries <- aus_retail %>%
filter(`Series ID` == 'A3349849A')
autoplot(myseries)
Why is multiplicative seasonality necessary for this series?
Multiplicative seasonality is necessary for this series because the seasonal variations are changing protional to the time series.
Apply Holt-Winters’ multiplicative method to the data. Experiment with making the trend damped.
ms_mod <- myseries %>% model('Multiplicative' = ETS(Turnover ~ error("M") + trend("A") + season("M")),
'Damped Multiplicative' = ETS(Turnover ~ error("M") + trend("Ad", phi = 0.75) + season("M")))
ms_fc <- ms_mod %>% forecast(h=20)
ms_fc %>% autoplot(aus_retail, level = NULL)
Compare the RMSE of the one-step forecasts from the two methods. Which do you prefer?
accuracy(ms_mod) %>% select('.model', 'RMSE')## # A tibble: 2 x 2
## .model RMSE
## <chr> <dbl>
## 1 Multiplicative 1.78
## 2 Damped Multiplicative 1.76The RMSE for the damped multiplicative model is lower so it would be preferred.
Check that the residuals from the best method look like white noise.
pref_mod <- myseries %>%
model('Damped Multiplicative' = ETS(Turnover ~ error("M") + trend("Ad", phi = 0.75) + season("M")))
pref_mod %>%
gg_tsresiduals() 
augment(pref_mod) %>% features(.innov, box_pierce, lag = 24)## # A tibble: 1 x 5
## State Industry .model bp_stat bp_pvalue
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Australian Capital Territory Cafes, restaurants and ~ Dampe~ 30.7 0.161augment(pref_mod) %>% features(.innov, ljung_box, lag = 24)## # A tibble: 1 x 5
## State Industry .model lb_stat lb_pvalue
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Australian Capital Territory Cafes, restaurants and ~ Dampe~ 31.8 0.131Both tests show the p value to be greater 0.05, which shows that the residuals are not distinguishable from white noise.
Now find the test set RMSE, while training the model to the end of 2010. Can you beat the seasonal naïve approach from Exercise 7 in Section 5.11?
train <- myseries %>%
filter(year(Month) <= 2010)
mod <- train %>%
model('Multiplicative' = ETS(Turnover ~ error("M") + trend("A") + season("M")),
'Seasonal Naive' = SNAIVE(Turnover))
fc <- mod %>%
forecast(new_data = anti_join(myseries, train))
fc %>% autoplot(myseries, level = NULL)
accuracy(mod) %>%
select(.type, .model, RMSE)## # A tibble: 2 x 3
## .type .model RMSE
## <chr> <chr> <dbl>
## 1 Training Multiplicative 1.38
## 2 Training Seasonal Naive 3.37fc %>% accuracy(myseries) %>%
select(.type, .model, RMSE)## # A tibble: 2 x 3
## .type .model RMSE
## <chr> <chr> <dbl>
## 1 Test Multiplicative 6.62
## 2 Test Seasonal Naive 8.42The Multiplicative model has the lower RMSE and forecasts better.
8.9
For the same retail data, try an STL decomposition applied to the Box-Cox transformed series, followed by ETS on the seasonally adjusted data. How does that compare with your best previous forecasts on the test set?
lam <- myseries %>%
features(Turnover, features = guerrero) %>%
pull(lambda_guerrero)
stl_decomp <- myseries %>%
model(stl_box = STL(box_cox(Turnover,lam) ~ season(window = "periodic"), robust = TRUE)) %>%
components()
stl_decomp %>% autoplot()
myseries$Turnover_sa <- stl_decomp$season_adjust
training <- myseries %>%
filter(year(Month) <= 2010)
model <- training %>%
model(ETS(Turnover_sa ~ error("M") + trend("A") + season("M")))
forecast <- model %>% forecast(new_data = anti_join(myseries, training))
model %>% accuracy() %>%
select(.model, .type, RMSE)## # A tibble: 1 x 3
## .model .type RMSE
## <chr> <chr> <dbl>
## 1 "ETS(Turnover_sa ~ error(\"M\") + trend(\"A\") + season(\"M\"))" Traini~ 0.375model %>% gg_tsresiduals() 
augment(model) %>% features(.innov, box_pierce, lag = 24)## # A tibble: 1 x 5
## State Industry .model bp_stat bp_pvalue
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Australian Capital Territory Cafes, restaurants and ~ "ETS(~ 37.3 0.0406augment(model) %>% features(.innov, ljung_box, lag = 24)## # A tibble: 1 x 5
## State Industry .model lb_stat lb_pvalue
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Australian Capital Territory Cafes, restaurants and ~ "ETS(~ 38.7 0.0291model %>% accuracy() %>%
select(.model, .type, RMSE)## # A tibble: 1 x 3
## .model .type RMSE
## <chr> <chr> <dbl>
## 1 "ETS(Turnover_sa ~ error(\"M\") + trend(\"A\") + season(\"M\"))" Traini~ 0.375forecast %>% accuracy(myseries) %>%
select(.model, .type, RMSE)## # A tibble: 1 x 3
## .model .type RMSE
## <chr> <chr> <dbl>
## 1 "ETS(Turnover_sa ~ error(\"M\") + trend(\"A\") + season(\"M\"))" Test 0.692