Online Learning

For brevity, we consider a univariate probabilistic forecast combination problem.

Let y be a vector of realized values of length T = 32. Additionally, we have K = 2 probabilistic expert forecasts for each observation in y. These probabilistic forecasts are equidistant grids of P quantiles. The goal is to combine the expert forecasts to obtain a combined forecast ỹ_{t|t − 1} for each observation y_t in y. Formally:

Let’s simulate this setting in R before we apply the combination algorithm.

set.seed(1)
T <- 2^5 # Observations
D <- 1 # Numer of variables
N <- 2 # Experts
P <- 99 # Size of probability grid
probs <- 1:P / (P + 1)

y <- matrix(rnorm(T)) # Realized observations

# Experts deviate in mean and standard deviation from true process
experts_mu <- c(-1, 3)
experts_sd <- c(1, 2)

experts <- array(dim = c(T, P, N)) # Expert predictions

for (t in 1:T) {
  experts[t, , 1] <- qnorm(probs, mean = experts_mu[1], sd = experts_sd[1])
  experts[t, , 2] <- qnorm(probs, mean = experts_mu[2], sd = experts_sd[2])
}

The situation can be depicted as follows:

Most parameters of online() contain sensible defaults. So, in this example, it is sufficient to provide the data and the probability grid.

library(profoc)

combination <- online(
  y = y,
  experts = experts,
  tau = probs
)

The code above will compute the CRPS Learning algorithm described in (Berrisch and Ziel 2021). This algorithm is based on Bernstein online aggregation (Wintenberger 2017) and uses the quantile loss to calculate weights on the probability grid. Other algorithms like ewa or ml-poly can be computed by setting the method argument accordingly.

Printing the result object will present the loss of the experts and the computed combination:

print(combination)
#>  Name        Loss     
#>  E1          0.4486700
#>  E2          0.9804200
#>  Combination 0.2925581

These are averages over time, the probability grid, and possibly all marginals. The losses of the Experts and the Forecaster can be accessed via combination$experts_loss and combination$forecaster_loss, respectively.

We continue by inspecting the weights that were assigned during the online learning process:

dim(combination$weights)
#> [1] 33  1 99  2

We can visualize the most recent weights with autoplot():

autoplot(combination)

Expert one receives high weights, particularly in the right tail. This is because the predictions of expert one are closer to the true DGP, especially in the right tail.

We currently offer tidy() methods for selected output elements (weights, predictions, forecaster_loss, and experts_loss). These methods return a tibble for further analysis. For example, we can easily plot the weights of both experts for selected quantiles over time with just a few lines of code using {dplyr} and {ggplot2}:

library(dplyr)
library(ggplot2)

tidy(combination$weights) |>
  filter(p %in% c(0.05, 0.5, 0.95)) |>
  ggplot(aes(x = t, y = w, col = k)) +
  geom_line(linewidth = 1) +
  facet_wrap(~p, ncol = 1)

Finally, we can inspect and visualize the predictions:

tidy(combination$predictions)
#> # A tibble: 3,168 × 4
#>        t d         p prediction
#>    <int> <chr> <dbl>      <dbl>
#>  1     1 D1     0.01     -2.49 
#>  2     1 D1     0.02     -2.08 
#>  3     1 D1     0.03     -1.82 
#>  4     1 D1     0.04     -1.63 
#>  5     1 D1     0.05     -1.47 
#>  6     1 D1     0.06     -1.33 
#>  7     1 D1     0.07     -1.21 
#>  8     1 D1     0.08     -1.11 
#>  9     1 D1     0.09     -1.01 
#> 10     1 D1     0.1      -0.922
#> # ℹ 3,158 more rows

tidy(combination$predictions) |>
  ggplot(aes(x = t, y = prediction, group = p, colour = p)) +
  geom_line() +
  scale_color_continuous(low = "#FFDD00", high = "#0057B7") +
  # A little hacky way to add the realized values
  geom_line(aes(x = t, y = rep(y, each = 99)),
    linetype = "dashed", col = "black", linewidth = 1
  )

If you want to learn more on using online() in production for predicting and updating, refer to vignette("production").