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Using online in Production

This vignette explains the use of predict() and update(). These are the two most important functions when using profoc in production. The predict() method is used to combine new expert forecasts using the most recent combination weights. This is useful if we combine new expert forecasts with the most recent combination weights, but new observations have yet to be realized. At a later point, update() can be used to update the combination weights by evaluating the realized observations. We assume that you followed the vignette("profoc") already. We will reuse the data and the model from there.

Combining new expert predictions

First, we create new expert predictions:

new_experts <- experts[T, , , drop = FALSE]

The default behavior of predict() updates the combination object. So, it can later be used to update the combination weights as realized values emerge. That is, predict() expands combination$predictions and returns the updated combination.

#> [1] 32  1 99

# Predict will expand combination$predictions
combination <- predict(combination,
  new_experts = new_experts

#> [1] 33  1 99

If you are only interested in the predictions, you can set update_model = FALSE. In this case, predict() solely returns the predictions:

predictions <- predict(combination,
  new_experts = new_experts,
  update_model = FALSE

#> [1]  1  1 99

Updating the model weights

As new realizations emerge, we can update the combination weights. This is done by update(). That is, update() expands combination$weights and returns the updated combination.

# New observation
new_y <- matrix(rnorm(1))
#> [1] 33  1 99  2

# Model Update
combination <-
    new_y = new_y

#> [1] 34  1 99  2

Summary on predict() and update()

As seen above, predict() and update() are closely related and usually called sequentially. In an only setting, we want to calculate the forecast (the combination) as soon as new expert predictions emerge. For that, we can use predict(). Later, as new observations are realized, we can update() the combination weights.

We designed to also work in non-standard scenarios. So if, for example, experts provide multi-step-ahead predictions, we can use predict() to combine all of them using the most recent combination weights. Afterward, one or multiple update() calls can be used to update the combination weights as new observations are realized. If we want to predict() and update() simultaneously, we can do this. We can pass the new expert predictions and observations to predict(). This will update the combination weights and predictions with only one call to predict().