A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017

An S4 package for dynamic combination of forecasting models

The package provided methods for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches.

Install the package using your R console:

`install.packages('tsensembler')`

or

`devtools::install_github('vcerqueira/tsensembler')`

```
# Using data of water consumption time series attached to the package
data("water_consumption")
embedding time series into a matrix
`dataset <- embed_timeseries(water_consumption, 5)`
# splitting data into train/test
train <- dataset[1:1000,]
test <- dataset[1001:1020, ]
# setting up base model parameters
specs <- model_specs(
learner = c("bm_ppr","bm_glm","bm_svr","bm_mars"),
learner_pars = list(
bm_glm = list(alpha = c(0, .5, 1)),
bm_svr = list(kernel = c("rbfdot", "polydot"),
C = c(1,3)),
bm_ppr = list(nterms = 4)
))
# building the ensemble
model <- ADE(target ~., train, specs)
# forecast next value and update base and meta models
# every three points;
# in the other points, only the weights are updated
predictions <- numeric(nrow(test))
for (i in seq_along(predictions)) {
predictions[i] <- predict(model, test[i, ])@y_hat
if (i %% 3 == 0) {
model <-
update_base_models(model,
rbind.data.frame(train, test[seq_len(i), ]))
model <- update_ade_meta(model, rbind.data.frame(train, test[seq_len(i), ]))
}
else
model <- update_weights(model, test[i, ])
}
point_forecast <- forecast(model, h = 5)
# setting up an ensemble of support vector machines
specs2 <-
model_specs(learner = c("bm_svr"),
learner_pars = list(
bm_svr = list(kernel = c("vanilladot", "polydot",
"rbfdot"),
C = c(1,3,6))
))
model <- DETS(target ~., train, specs2)
preds <- predict(model, test)@y_hat
```

Any bug report or suggestion please contact me at [email protected]