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Removed unnecessary blank lines

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robjhyndman committed Jan 14, 2020
1 parent 85e34a1 commit fddf51e833248609fba2c2250db93d6214eb19dc
@@ -123,7 +123,6 @@ install.packages(c(
\vspace*{.6cm}\begin{alertblock}{}{\centerline{\Large\textbf{\url{bit.ly/fable2020}}}}
\end{alertblock}


## Find me at ...
\Large\vspace*{2.5cm}
\begin{flushright}
@@ -138,6 +137,3 @@ install.packages(c(

\end{flushright}




@@ -264,9 +264,6 @@ where
* $\bm{b}_t$ is a vector of the most disaggregated series at time $t$
* $\bS$ is a ``summing matrix'' containing the aggregation constraints.
## Hierarchical time series
\begin{minipage}{4cm}\vspace*{0.2cm}
@@ -438,7 +435,6 @@ fc %>%
autoplot(tourism_agg, level = 95)
```


## Example: Australian tourism
\fontsize{12}{12.5}\sf

@@ -172,7 +172,6 @@ prettify(p1,

2. Produce a calendar plot for the `pedestrian` data from one location and one year.


# Seasonal or cyclic?

## Time series patterns
@@ -321,7 +320,6 @@ new_production %>%

\vspace*{10cm}


## ACF

```{r, fig.height=4, echo=TRUE}
@@ -346,7 +344,6 @@ holidays %>%
autoplot()
```


## Trend and seasonality in ACF plots

- When data have a trend, the autocorrelations for small lags tend to be large and positive.
@@ -481,7 +478,6 @@ wn %>% ACF(y)

\fontsize{10}{11}\sf\tabcolsep=0.1cm


```{r, echo=FALSE}
wn %>%
ACF(y, lag_max = 10) %>%
@@ -549,12 +545,10 @@ These show the series is **not a white noise series**.
# Lab Session 5
## Lab Session 5


You can compute the daily changes in the Google stock price in 2018 using

\fontsize{10.5}{13}\sf


```{r, eval = FALSE}
dgoog <- gafa_stock %>%
filter(Symbol == "GOOG", year(Date) >= 2018) %>%
@@ -47,7 +47,6 @@ global_economy %>%

Consider the GDP information in `global_economy`. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time?


# Inflation adjustments

## Inflation adjustments
@@ -73,7 +72,6 @@ print_retail %>%
ggtitle("Turnover: Australian print media industry")
```


## Inflation adjustments
\fontsize{10}{10}\sf

@@ -205,7 +203,6 @@ food %>%

\fontsize{13}{15}\sf


```{r food-lambda, echo=TRUE}
food %>%
features(Turnover, features = guerrero)
@@ -217,7 +214,6 @@ food %>%
* Always check the results.
* A low value of $\lambda$ can give extremely large prediction intervals.


## Box-Cox transformations
\fontsize{13}{14}\sf

@@ -23,7 +23,6 @@ library(purrr)
elecequip <- as_tsibble(fpp2::elecequip)
```


# Time series decompositions

## Time series decomposition
@@ -44,7 +43,6 @@ where & $y_t=$ & data at period $t$ \\
& $R_t=$ & remainder component at period $t$
\end{tabular}


## STL decomposition

\fontsize{13}{14}\sf
@@ -59,7 +57,6 @@ where & $y_t=$ & data at period $t$ \\
* Take logs to get multiplicative decomposition.
* Use Box-Cox transformations to get other decompositions.


## Decomposition dable

\fontsize{10}{11}\sf
@@ -86,7 +86,6 @@ tourism %>%
facet_grid(vars(State, Region, Purpose))
```


## Feature extraction and statistics
\fontsize{9}{9}\sf

@@ -114,7 +113,6 @@ tourism %>%
* Use ``GGally::ggpairs()`` to look at the relationships between the STL-based features. You might wish to change `seasonal_peak_year` and `seasonal_trough_year` to factors.
* Which is the peak quarter for holidays in each state?


## Feature extraction and statistics

\fontsize{9}{10}\sf
@@ -184,7 +182,6 @@ All features from the feasts package
\end{alertblock}
\end{textblock}


## Feature extraction and statistics
\fontsize{9}{9}\sf

@@ -222,11 +219,9 @@ pcs %>% ggplot(aes(x=.fittedPC1, y=.fittedPC2)) +
\placefig{4}{2.6}{height=6.4cm, width=12cm}{pca1}
\vspace*{10cm}


## Feature extraction and statistics
\fontsize{9}{9}\sf


\begin{textblock}{3.3}(.4,3)
\begin{alertblock}{}\fontsize{10}{12}\sf
Principal components based on all features from the feasts package
@@ -241,11 +236,9 @@ pcs %>% ggplot(aes(x=.fittedPC1, y=.fittedPC2, col=State)) +
\placefig{4}{2.6}{height=6.4cm, width=12cm}{pca2}
\vspace*{10cm}


## Feature extraction and statistics
\fontsize{9}{9}\sf


\begin{textblock}{3.3}(.4,3)
\begin{alertblock}{}\fontsize{10}{12}\sf
Principal components based on all features from the feasts package
@@ -261,7 +254,6 @@ pcs %>% ggplot(aes(x=.fittedPC1, y=.fittedPC2, col=Purpose)) +
\only<2>{\placefig{4}{2.6}{height=6.4cm, width=12cm}{pca4}}
\vspace*{10cm}


## Feature extraction and statistics
\fontsize{8}{8}\sf

@@ -510,7 +510,6 @@ augment(fit) %>%

\vspace*{-0.3cm}


```{r dj9, echo=TRUE}
# lag=h and dof=K
augment(fit) %>%
@@ -31,7 +31,6 @@ austa <- as_tsibble(fpp2::austa) %>%
rename(Year = index, Visitors = value)
```


# Exponential smoothing

## Pharmaceutical Benefits Scheme
@@ -58,7 +57,6 @@ austa <- as_tsibble(fpp2::austa) %>%
* Although monthly data available for 10 years, data are aggregated to annual values, and only the first three years are used in estimating the forecasts.
* All forecasts being done with the \texttt{FORECAST} function in MS-Excel!


## Historical perspective

* Developed in the 1950s and 1960s as methods (algorithms) to produce point forecasts.
@@ -67,8 +65,6 @@ austa <- as_tsibble(fpp2::austa) %>%
* Need to choose best values for the smoothing parameters (and initial states).
* Equivalent ETS state space models developed in the 1990s and 2000s.



## A model for levels, trends, and seasonalities
\fontsize{13}{14}\sf

@@ -306,7 +302,6 @@ fit <- holidays %>% model(ets = ETS(Trips))
fit
```
## Example: Australian holiday tourism
\fontsize{9}{10}\sf
@@ -450,7 +445,6 @@ Find an ETS model for the Gas data from `aus_production`.
* Why is multiplicative seasonality necessary here?
* Experiment with making the trend damped. Does it improve the forecasts?


# Non-Gaussian forecast distributions

## Non-Gaussian forecast distributions
@@ -34,7 +34,6 @@ austa <- as_tsibble(fpp2::austa) %>%
rename(Year = index, Visitors = value)
```


# ARIMA models

## ARIMA models
@@ -130,7 +129,6 @@ p2 <- tsibble(idx = seq_len(100), sim = arima.sim(list(ma = c(-1, +0.8)), n = 10
gridExtra::grid.arrange(p1, p2, nrow = 1)
```


## ARIMA models

\begin{block}{Autoregressive Moving Average models:}\vspace*{-0.4cm}
@@ -190,7 +188,6 @@ fit %>%
\end{textblock}}
\vspace*{3cm}


## Understanding ARIMA models
\fontsize{14}{16}\sf

@@ -209,7 +206,6 @@ fit %>%
* The higher the value of $d$, the more rapidly the prediction intervals increase in size.
* For $d=0$, the long-term forecast standard deviation will go to the standard deviation of the historical data.


## Example: National populations
\fontsize{9}{9}\sf

@@ -377,7 +377,6 @@ forecast(fit, h = "3 years") %>%
autoplot(gasoline)
```


# Lab Session 19
## Lab Session 19

@@ -451,7 +450,6 @@ insurance %>%
labs(title = "Insurance advertising and quotations")
```


## Example: Insurance quotes and TV adverts
\fontsize{10}{10}\sf

@@ -461,14 +459,14 @@ fit <- insurance %>%
mutate(Quotes = c(NA, NA, NA, Quotes[4:40])) %>%
model(
ARIMA(Quotes ~ pdq(d = 0) + TV.advert),
ARIMA(Quotes ~ pdq(d = 0) + TV.advert +
ARIMA(Quotes ~ pdq(d = 0) + TV.advert +
lag(TV.advert)),
ARIMA(Quotes ~ pdq(d = 0) + TV.advert +
ARIMA(Quotes ~ pdq(d = 0) + TV.advert +
lag(TV.advert) +
lag(TV.advert, 2)),
ARIMA(Quotes ~ pdq(d = 0) + TV.advert +
ARIMA(Quotes ~ pdq(d = 0) + TV.advert +
lag(TV.advert) +
lag(TV.advert, 2) +
lag(TV.advert, 2) +
lag(TV.advert, 3))
)
```
@@ -22,7 +22,6 @@ state_tourism <- mytourism %>%
summarise(Trips = sum(Trips)) %>%
ungroup()


# Lab Session 2

aus_production %>% autoplot(Bricks)
@@ -47,7 +46,6 @@ snowy %>% autoplot(Trips)
snowy %>% gg_season(Trips)
snowy %>% gg_subseries(Trips)


# Lab Session 4

aus_production %>% gg_lag(Bricks)
@@ -120,10 +118,9 @@ global_economy %>%

holidays %>%
model(STL(Trips ~ season(window = 13) + trend(window = 21))) %>%
components() %>%
components() %>%
autoplot()


# Lab Session 7

global_economy %>%
@@ -147,7 +144,7 @@ aus_production %>%

canadian_gas %>%
model(STL(Volume ~ season(window=7) + trend(window=11))) %>%
components() %>%
components() %>%
autoplot()

## Changing the size of the windows changes the trend and seasonal components
@@ -156,12 +153,12 @@ canadian_gas %>%

canadian_gas %>%
model(STL(Volume ~ season(window=7) + trend(window=11))) %>%
components() %>%
components() %>%
gg_season(season_year)

canadian_gas %>%
model(STL(Volume ~ season(window=7) + trend(window=11))) %>%
components() %>%
components() %>%
select(index, season_adjust) %>%
autoplot(season_adjust)

@@ -183,7 +180,6 @@ tourism %>%
features(Trips, feat_stl) %>%
select(State, seasonal_peak_year)


# Lab Session 10

## Two series have all zeros, so we will drop these to avoid problems in the later calculations
@@ -253,7 +249,6 @@ augment(beer_model) %>%

# Lab Session 13


hh_budget_train <- hh_budget %>%
filter(Year <= max(Year) - 4)

@@ -288,11 +283,11 @@ aus_takeaway_forecast %>%
# Lab Session 14

global_economy %>%
filter(Country == "China") %>%
filter(Country == "China") %>%
autoplot(GDP)

global_economy %>%
filter(Country == "China") %>%
filter(Country == "China") %>%
model(
ets = ETS(GDP),
ets_damped = ETS(GDP ~ trend("Ad")),
@@ -331,7 +326,6 @@ us_gdp_model %>%
forecast(h = "10 years") %>%
autoplot(us_gdp)


# Lab Session 17

tourism_models <- tourism %>%

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