Pierre Gaillard, Yannig Goude

- Setting: when is the package
`opera`

useful? - Installation
- Example: predict the weekly electricity consumption.
- Meta

`opera`

is a R package that provides several algorithms to perform robust online prediction of time series with the help of expert advice. In this vignette, we provide an example of how to use the package.

`opera`

useful?Consider a sequence of real bounded observations

[ \widehat y_t = \sum_{k=1}^K p_{k,t} x_{k,t} \,.](https://latex.codecogs.com/png.latex?%0A%20%20%5Cwidehat%20y_t%20%3D%20%5Csum_%7Bk%3D1%7D%5EK%20p_%7Bk%2Ct%7D%20x_%7Bk%2Ct%7D%20%5C%2C.%0A " y_t = *{k=1}^K p*{k,t} x_{k,t} ,. ")

These algorithms come with finite time worst-case guarantees. The monograph of Cesa-Bianchi and Lugisi (2006) gives a complete introduction to the setting of prediction of arbitrary sequences with the help of expert advice.

The package `opera`

provides three important functions: `mixture`

to build the algorithm object, `predict`

to make a prediction by using the algorithm, and `oracle`

to evaluate the performance of the experts and compare the performance of the combining algorithm.

opera is now available on CRAN, so you can install it with:

You can also install the development version of opera with the package devtools:

You may be asked to install additional necessary packages. You can install the package vignette by setting the option: build_vignettes = TRUE.

Here, we provide a concrete example on how to use the package. To do so, we consider an electricity forecasting data set that includes weekly observations of the French electric load together with several covariates: the temperature, calendar information, and industrial production indexes. The data set is provided by the French National Institute of Statistics and Economic Studies (Insee).

First, we load the data and we cut it into two subsets: a training set used to build the experts (base forecasting methods) and a testing set (here the last two years) used to evaluate the performance and to run the combining algorithms.

```
data(electric_load)
attach(electric_load)
idx_data_test <- 620:nrow(electric_load)
data_train <- electric_load[-idx_data_test, ]
data_test <- electric_load[idx_data_test, ]
```

The data is displayed in the following figures.

Here, we build three base forecasting methods to be combined later.

- A generalized additive model using the
`mgcv`

package:

```
library(mgcv)
gam.fit <- gam(Load ~ s(IPI) + s(Temp) + s(Time, k=3) +
s(Load1) + as.factor(NumWeek), data = data_train)
gam.forecast <- predict(gam.fit, newdata = data_test)
```

- A medium term generalized additive model followed by an autoregressive short-term correction.

```
# medium term model
medium.fit <- gam(Load ~ s(Time,k=3) + s(NumWeek) + s(Temp) + s(IPI), data = data_train)
electric_load$Medium <- c(predict(medium.fit), predict(medium.fit, newdata = data_test))
electric_load$Residuals <- electric_load$Load - electric_load$Medium
# autoregressive correction
ar.forecast <- numeric(length(idx_data_test))
for (i in seq(idx_data_test)) {
ar.fit <- ar(electric_load$Residuals[1:(idx_data_test[i] - 1)])
ar.forecast[i] <- as.numeric(predict(ar.fit)$pred) + electric_load$Medium[idx_data_test[i]]
}
```

- A gradient boosting model using
`caret`

package

```
library(caret)
gbm.fit <- train(Load ~ IPI + IPI_CVS + Temp + Temp1 + Time + Load1 + NumWeek, data = data_train, method = "gbm")
gbm.forecast <- predict(gbm.fit, newdata = data_test)
```

Once the expert forecasts have been created (note that they can also be formed online), we build the matrix of expert and the time series to be predicted online

```
Y <- data_test$Load
X <- cbind(gam.forecast, ar.forecast, gbm.forecast)
matplot(cbind(Y, X), type = "l", col = 1:6, ylab = "Weekly load",
xlab = "Week", main = "Expert forecasts and observations")
```

To evaluate the performance of the experts and see if the aggregation rules may perform well, you can look at the oracles (rules that are used only for analysis and cannot be design online).

```
oracle.convex <- oracle(Y = Y, experts = X, loss.type = "square", model = "convex")
print(oracle.convex)
plot(oracle.convex)
```

```
#> Call:
#> oracle.default(Y = Y, experts = X, model = "convex", loss.type = "square")
#>
#> Coefficients:
#> gam ar gbm
#> 0.719 0.201 0.0799
#>
#> rmse mape
#> Best expert oracle: 1480 0.0202
#> Uniform combination: 1560 0.0198
#> Best convex oracle: 1440 0.0193
```

The parameter `loss.type`

defines the evaluation criterion. It can be either the square loss, the percentage loss, the absolute loss, or the pinball loss to perform quantile regression.

The parameter `model`

defines the oracle to be calculated. Here, we computed the best fixed convex combination of expert (i.e., with non-negative weights that sum to one).

The first step consists in initializing the algorithm by defining the type of algorithm (Ridge regression, exponentially weighted average forecaster,…), the possible parameters, and the evaluation criterion. If no parameter is defined by the user, all parameters will be calibrated online by the algorithm. Bellow, we define the ML-Poly algorithm, evaluated by the square loss.

Then, you can perform online predictions by using the `predict`

method. At each time, step the aggregation rule forms a new prediction and update the procedure.

```
MLpol <- MLpol0
for (i in 1:length(Y)) {
MLpol <- predict(MLpol, newexperts = X[i, ], newY = Y[i])
}
```

The results can be displayed with method `summary`

and `plot`

.

```
summary(MLpol)
#> Aggregation rule: MLpol
#> Loss function: square loss
#> Gradient trick: TRUE
#> Coefficients:
#> gam ar gbm
#> 0.577 0.423 0
#>
#> Number of experts: 3
#> Number of observations: 112
#> Dimension of the data: 1
#>
#> rmse mape
#> MLpol 1460 0.0192
#> Uniform 1560 0.0198
```

The same results can be obtained more directly:

- by giving the whole time series to
`predict`

specifying`online = TRUE`

to perform online prediction.

- or directly to the function mixture, when building the aggregation rule

The vector of predictions formed by the mixture can be obtained through the output prediction.

Note that these predictions were obtained sequentially by the algorithm by updating the model coefficients after each data. It is worth to emphasize that each observation is used to get the prediction of the next observations but not itself (nor past observations).

In real-world situations, predictions should be produced without having access to the observed outputs. There are several solutions to get them with `opera`

.

Using the `predict`

method with the arguments * `online = FALSE`

: the model coefficients are not updated during the prediction. * `type = response`

: the predictions are returned and not an updated model.

```
newexperts <- X[1:3, ] # Experts forecasts to predict 3 new points
pred <- predict(MLpol, newexperts = newexperts, online = FALSE, type = 'response')
```

`#> [1] 64397.10 61317.41 63571.59`

The same result can be easily obtained by using the last model coefficients to perform the weighted average of the expert advice.

`#> [1] 64397.10 61317.41 63571.59`

In some situations, the aggregation model cannot be updated at each step due to an operational constraint. One must predict the time-series

Let’s say that in the above example, instead of making a weekly prediction, we need to predict every four weeks, the consumption of the next four weeks. Below we show two different ways to use the `opera`

package to do this.

Note that the outputs `opera`

. In this case, the argument `Y`

provided to the function `mixture`

should be a matrix, where each line corresponds to the output observed at time `experts`

must be an array of dimension `experts[t,:,k]`

is the prediction made by expert

Another useful application of this feature is block by block prediction. Consider the above exemple of electricity consumption to be predicted every four weeks. This can be easily done by transforming the original time series into a 4 dimensional time series, where each row corresponds to the four weeks to predict after each update.

The four-week-by-four-week predictions can then be obtained by directly using the `mixture`

function as we did earlier.

The predictions can finally be transformed back to a regular one dimensional time-series by using the function `blockToSeries`

.

`online = FALSE`

optionAnother equivalent solution is to use the `online = FALSE`

option in the predict function. The latter ensures that the model coefficients are not updated between the next four weeks to forecast.

```
MLpolBlock <- MLpol0
d = 4
n <- length(Y)/d
for (i in 0:(n-1)) {
idx <- 4*i + 1:4 # next four weeks to be predicted
MLpolBlock <- predict(MLpolBlock, newexperts = X[idx, ], newY = Y[idx], online = FALSE)
}
```

As we see below, the coefficients are updated every four lines only.

```
#> [,1] [,2] [,3]
#> [1,] 0.3333333 0.3333333 0.3333333
#> [2,] 0.3333333 0.3333333 0.3333333
#> [3,] 0.3333333 0.3333333 0.3333333
#> [4,] 0.3333333 0.3333333 0.3333333
#> [5,] 0.1437215 0.0000000 0.8562785
#> [6,] 0.1437215 0.0000000 0.8562785
```

- Please report any issues or bugs.
- License: LGPL