Features

Here we will go through how to calculate features in R and show some examples of features you can calculate for time series using the feasts package. You find more details in Chapter 4.1-4.3 of Hyndman and Athanasopoulos(2021). The code used in this lecture is an adaptation of what you find in these sections.

Code used in video

# --- Time series features ---
library(fpp3)
library(tidyverse)
theme_set(theme_bw() +
            theme(panel.grid.minor = element_blank(),
                  panel.grid.major = element_blank(),
                  strip.background = element_rect(fill ="white",
                                                  color = "transparent")))
 
# -- Features: mean --
tourism %>% 
  features(Trips, list(mean = mean)) %>%
  arrange(mean)

# A tibble: 304 × 4
   Region          State              Purpose   mean
   <chr>           <chr>              <chr>    <dbl>
 1 Kangaroo Island South Australia    Other    0.340
 2 MacDonnell      Northern Territory Other    0.449
 3 Wilderness West Tasmania           Other    0.478
 4 Barkly          Northern Territory Other    0.632
 5 Clare Valley    South Australia    Other    0.898
 6 Barossa         South Australia    Other    1.02 
 7 Kakadu Arnhem   Northern Territory Other    1.04 
 8 Lasseter        Northern Territory Other    1.14 
 9 Wimmera         Victoria           Other    1.15 
10 MacDonnell      Northern Territory Visiting 1.18 
# ℹ 294 more rows

# -- Features: mean, sd, 2.5 and 97.5 percentiles --
tourism %>% 
  features(Trips, list(mean = mean,
                       sd   = sd,
                       p    = ~quantile(., probs = c(0.025, 0.975)))) %>%
  arrange(desc(sd))

# A tibble: 304 × 7
   Region                 State           Purpose  mean    sd `p_2.5%` `p_97.5%`
   <chr>                  <chr>           <chr>   <dbl> <dbl>    <dbl>     <dbl>
 1 South Coast            New South Wales Holiday  495. 170.     285.       812.
 2 North Coast NSW        New South Wales Holiday  588. 117.     404.       815.
 3 Sydney                 New South Wales Busine…  602. 117.     402.       884.
 4 Great Ocean Road       Victoria        Holiday  281. 116.     135.       545.
 5 Melbourne              Victoria        Holiday  507. 103.     354.       736.
 6 Peninsula              Victoria        Holiday  185.  96.7     70.8      458.
 7 Australia's South West Western Austra… Holiday  309.  95.3    179.       541.
 8 Melbourne              Victoria        Visiti…  619.  93.6    472.       807.
 9 Brisbane               Queensland      Visiti…  493.  90.6    344.       663.
10 Sydney                 New South Wales Visiti…  747.  89.6    564.       916.
# ℹ 294 more rows

# -- dplyr equivalent: --
tourism  %>% 
  as_tibble() %>% 
  group_by(Region,State,Purpose) %>%
  summarize(mean = mean(Trips)) %>%
  arrange(mean)

# A tibble: 304 × 4
# Groups:   Region, State [76]
   Region          State              Purpose   mean
   <chr>           <chr>              <chr>    <dbl>
 1 Kangaroo Island South Australia    Other    0.340
 2 MacDonnell      Northern Territory Other    0.449
 3 Wilderness West Tasmania           Other    0.478
 4 Barkly          Northern Territory Other    0.632
 5 Clare Valley    South Australia    Other    0.898
 6 Barossa         South Australia    Other    1.02 
 7 Kakadu Arnhem   Northern Territory Other    1.04 
 8 Lasseter        Northern Territory Other    1.14 
 9 Wimmera         Victoria           Other    1.15 
10 MacDonnell      Northern Territory Visiting 1.18 
# ℹ 294 more rows

# -- ACF features --
tourism %>% 
  features(Trips, feat_acf) 

# A tibble: 304 × 10
   Region         State Purpose     acf1 acf10 diff1_acf1 diff1_acf10 diff2_acf1
   <chr>          <chr> <chr>      <dbl> <dbl>      <dbl>       <dbl>      <dbl>
 1 Adelaide       Sout… Busine…  0.0333  0.131     -0.520       0.463     -0.676
 2 Adelaide       Sout… Holiday  0.0456  0.372     -0.343       0.614     -0.487
 3 Adelaide       Sout… Other    0.517   1.15      -0.409       0.383     -0.675
 4 Adelaide       Sout… Visiti…  0.0684  0.294     -0.394       0.452     -0.518
 5 Adelaide Hills Sout… Busine…  0.0709  0.134     -0.580       0.415     -0.750
 6 Adelaide Hills Sout… Holiday  0.131   0.313     -0.536       0.500     -0.716
 7 Adelaide Hills Sout… Other    0.261   0.330     -0.253       0.317     -0.457
 8 Adelaide Hills Sout… Visiti…  0.139   0.117     -0.472       0.239     -0.626
 9 Alice Springs  Nort… Busine…  0.217   0.367     -0.500       0.381     -0.658
10 Alice Springs  Nort… Holiday -0.00660 2.11      -0.153       2.11      -0.274
# ℹ 294 more rows
# ℹ 2 more variables: diff2_acf10 <dbl>, season_acf1 <dbl>

# -- STL features --
tourism %>%
  features(Trips, feat_stl) %>%
  ggplot(aes(x = trend_strength, y=seasonal_strength_year,
             color = Purpose)) + 
  geom_point() + 
  facet_wrap(vars(State))

# -- Time series with strongest seasonal component: --
tourism %>%
  features(Trips, feat_stl) %>%
  filter(
    seasonal_strength_year == max(seasonal_strength_year)
  ) %>%
  left_join(tourism, by = c("Region","State","Purpose")) %>%
  ggplot(aes(x=Quarter, y = Trips)) + geom_line() + 
  facet_grid(vars(Region,State,Purpose))