Visualization of Time Series Data

Alexander Häußer

Dezember 2021

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("ahaeusser/tscv")

# Load relevant packages
library(tscv)
library(tidyverse)
library(tsibble)

Data

series_id = "bidding_zone"
value_id = "value"
index_id = "time"

context <- list(
  series_id = series_id,
  value_id = value_id,
  index_id = index_id
)

# Prepare data set
main_frame <- elec_price %>%
  filter(bidding_zone %in% c("DE", "FR", "NO1", "SE1"))

main_frame
#> # A tibble: 70,176 x 5
#>    time                item            unit      bidding_zone  value
#>    <dttm>              <chr>           <chr>     <chr>         <dbl>
#>  1 2019-01-01 00:00:00 Day-ahead Price [EUR/MWh] DE            10.1 
#>  2 2019-01-01 01:00:00 Day-ahead Price [EUR/MWh] DE            -4.08
#>  3 2019-01-01 02:00:00 Day-ahead Price [EUR/MWh] DE            -9.91
#>  4 2019-01-01 03:00:00 Day-ahead Price [EUR/MWh] DE            -7.41
#>  5 2019-01-01 04:00:00 Day-ahead Price [EUR/MWh] DE           -12.6 
#>  6 2019-01-01 05:00:00 Day-ahead Price [EUR/MWh] DE           -17.2 
#>  7 2019-01-01 06:00:00 Day-ahead Price [EUR/MWh] DE           -15.1 
#>  8 2019-01-01 07:00:00 Day-ahead Price [EUR/MWh] DE            -4.93
#>  9 2019-01-01 08:00:00 Day-ahead Price [EUR/MWh] DE            -6.33
#> 10 2019-01-01 09:00:00 Day-ahead Price [EUR/MWh] DE            -4.93
#> # ... with 70,166 more rows

Line charts

# Example 1 -------------------------------------------------------------------

main_frame %>%
  plot_line(
    x = time,
    y = value,
    color = bidding_zone,
    facet_var = bidding_zone,
    title = "Day-ahead Electricity Spot Price",
    subtitle = "2019-01-01 to 2020-12-31",
    xlab = "Time",
    ylab = "[EUR/MWh]",
    caption = "Data: ENTSO-E Transparency"
    )

# Example 2 -------------------------------------------------------------------

main_frame %>%
  plot_line(
    x = time,
    y = value,
    color = bidding_zone,
    title = "Day-ahead Electricity Spot Price",
    subtitle = "2019-01-01 to 2020-12-31",
    xlab = "Time",
    ylab = "[EUR/MWh]",
    caption = "Data: ENTSO-E Transparency"
    )

Bar charts

# Estimate sample partial autocorrelation function
corr_pacf <- estimate_pacf(
  .data = main_frame,
  context = context,
  lag_max = 30
)

corr_pacf
#> # A tibble: 120 x 5
#>    bidding_zone type    lag    value sign 
#>    <chr>        <chr> <int>    <dbl> <lgl>
#>  1 DE           PACF      1  0.948   TRUE 
#>  2 DE           PACF      2 -0.437   TRUE 
#>  3 DE           PACF      3  0.00539 FALSE
#>  4 DE           PACF      4  0.0735  TRUE 
#>  5 DE           PACF      5  0.0735  TRUE 
#>  6 DE           PACF      6  0.0898  TRUE 
#>  7 DE           PACF      7  0.119   TRUE 
#>  8 DE           PACF      8  0.0786  TRUE 
#>  9 DE           PACF      9  0.0820  TRUE 
#> 10 DE           PACF     10  0.0469  TRUE 
#> # ... with 110 more rows

# Visualize PACF as correlogram
corr_pacf %>%
  plot_bar(
    x = lag,
    y = value,
    color = sign,
    facet_var = bidding_zone,
    position = "dodge",
    title = "Sample autocorrelation function",
    xlab = "Lag",
    ylab = "Correlation",
    caption = "Data: ENTSO-E Transparency"
  )

Distributions

Histograms

# Example 1 -------------------------------------------------------------------

main_frame %>%
  plot_histogram(
    x = value,
    color = bidding_zone,
    title = "Day-ahead Electricity Spot Price",
    xlab = "[EUR/MWh]",
    ylab = "Frequency",
    caption = "Data: ENTSO-E Transparency"
    )
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Example 2 -------------------------------------------------------------------

main_frame %>%
  plot_histogram(
    x = value,
    color = bidding_zone,
    facet_var = bidding_zone,
    facet_nrow = 1,
    title = "Day-ahead Electricity Spot Price",
    xlab = "[EUR/MWh]",
    ylab = "Frequency",
    caption = "Data: ENTSO-E Transparency"
    )
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Density

# Example 1 -------------------------------------------------------------------
main_frame %>%
  plot_density(
    x = value,
    color = bidding_zone,
    title = "Day-ahead Electricity Spot Price",
    xlab = "[EUR/MWh]",
    ylab = "Density",
    caption = "Data: ENTSO-E Transparency"
    )

# Example 2 -------------------------------------------------------------------
main_frame %>%
  plot_density(
    x = value,
    color = bidding_zone,
    facet_var = bidding_zone,
    facet_nrow = 1,
    title = "Day-ahead Electricity Spot Price",
    xlab = "[EUR/MWh]",
    ylab = "Density",
    caption = "Data: ENTSO-E Transparency"
    )

QQ-Plot

# Example 1 -------------------------------------------------------------------
main_frame %>%
  plot_qq(
    x = value,
    color = bidding_zone,
    title = "Day-ahead Electricity Spot Price",
    xlab = "Theoretical Quantile",
    ylab = "Sample Quantile",
    caption = "Data: ENTSO-E Transparency"
    )

# Example 2 -------------------------------------------------------------------
main_frame %>%
  plot_qq(
    x = value,
    color = bidding_zone,
    facet_var = bidding_zone,
    title = "Day-ahead Electricity Spot Price",
    xlab = "Theoretical Quantile",
    ylab = "Sample Quantile",
    caption = "Data: ENTSO-E Transparency"
    )