tsensembler (version 0.1.0)

tsensembler: Dynamic Ensembles for Time Series Forecasting

Description

This package implements ensemble methods for time series forecasting tasks. Dynamically combining different forecasting models is a common approach to tackle these problems.

Details

The main methods in tsensembler are in ADE-class and DETS-class:

ADE

Arbitrated Dynamic Ensemble (ADE) is an ensemble approach for dynamically combining forecasting models using a metalearning strategy called arbitrating. A meta model is trained for each base model in the ensemble. Each meta-learner is specifically designed to model the error of its associate across the time series. At forecasting time, the base models are weighted according to their degree of competence in the input observation, estimated by the predictions of the meta models

DETS

Dynamic Ensemble for Time Series (DETS) is similar to ADE in the sense that it adaptively combines the base models in an ensemble for time series forecasting. DETS follows a more traditional approach for forecaster combination. It pre-trains a set of heterogeneous base models, and at run-time weights them dynamically according to recent performance. Like ADE, the ensemble includes a committee, which dynamically selects a subset of base models that are weighted with a non-linear function

The ensemble methods can be used to predict new observations or forecast future values of a time series. They can also be updated using generic functions (check see also section).

References

Cerqueira, Vitor; Torgo, Luis; Pinto, Fabio; and Soares, Carlos. "Arbitrated Ensemble for Time Series Forecasting" to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017.

V. Cerqueira, L. Torgo, and C. Soares, <U+201C>Arbitrated ensemble for solar radiation forecasting,<U+201D> in International Work-Conference on Artificial Neural Networks. Springer, 2017, pp. 720<U+2013>732

Cerqueira, Vitor; Torgo, Luis; Oliveira, Mariana, and Bernhard Pfahringer. "Dynamic and Heterogeneous Ensembles for Time Series Forecasting." Data Science and Advanced Analytics (DSAA), 2017 IEEE International Conference on. IEEE, 2017.

See Also

ADE-class for setting up an ADE model; and DETS-class for setting up an DETS model; see also update_weights and update_base_models to check the generic function for updating the predictive models in an ensemble.

Examples

# NOT RUN {
# }
# NOT RUN {
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

# }
# NOT RUN {

# }