Features

Time Series Features

Statistics computed on time series data are called time series features.

library(pacman)
p_load(tidyverse, fpp3)

We will examine the time series data in the Tourism dateset.

data(tourism)
tourism

tourism |> features(Trips, quantile)

Note the list can be used for many statistics.

tourism |> features(Trips, list(mean = mean, median = median, sd = sd, min = min, max = max))

Lets look at the first year of the data.

tourism |> filter(Quarter <= yearquarter("1998 Q4")) |> 
  features(Trips, quantile)

ACF features

The correlations in time are all time series features.

tourism |> ACF()

Response variable not specified, automatically selected `var = Trips`

tourism |> filter(Region == "Adelaide", State == "South Australia", Purpose == "Holiday") |> 
  ACF() 

Response variable not specified, automatically selected `var = Trips`

tourism |> filter(Region == "Adelaide", State == "South Australia", Purpose == "Holiday") |> 
  ACF() |> 
  autoplot()

Response variable not specified, automatically selected `var = Trips`

tourism |> filter(Region == "Adelaide", State == "South Australia", Purpose == "Holiday") |> 
  autoplot() 

Plot variable not specified, automatically selected `.vars = Trips`

Compute most of the features.

tourism |>
  features(Trips, feat_stl)

tourism |> filter(Region == "Adelaide", State == "South Australia", Purpose == "Holiday") |> 
  features(Trips, feat_stl) 

Use the features to identify a time series

We can then use these features in plots to identify what type of series are heavily trended and what are most seasonal.

tourism |>
  features(Trips, feat_stl) |>
  ggplot(aes(x = trend_strength, y = seasonal_strength_year, col = Purpose)) +
  geom_point() +
  facet_wrap(vars(State))

Find the year with the maximum seasonal strength.

tourism |>
  features(Trips, feat_stl) |>
  filter(seasonal_strength_year == max(seasonal_strength_year)) |>
  left_join(tourism, by = c("State", "Region", "Purpose")) |>
  ggplot(aes(x = Quarter, y = Trips)) +
  geom_line() +
  facet_grid(vars(State, Region, Purpose))

Full feature set

tourism_features <- tourism |>
  features(Trips, feature_set(pkgs = "feasts"))
tourism_features

For the homework we will look at the PBS data.

PBS |> features(Cost, list(mean = mean, sd = sd))