# 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

nall_kernels <- c("rbfdot","polydot","vanilladot","laplacedot")

pars_predictors <- list(
  bm_gaussianprocess = list(kernel = nall_kernels, tol = c(.001)),
  bm_svr = list(kernel = nall_kernels, C=c(1, 5), epsilon=c(.1,0.01)),
  bm_ppr = list(nterms = c(2,4),
                sm.method = c("supsmu","gcvspline")),
  bm_mars = list(degree = c(1, 3), nk = c(10,20),
                 thresh=c(0.001),
                 pmethod=c("forward")),
  bm_glm = list(alpha = c(0,.25,.5,.75,1),
                family = c("gaussian")),
  bm_randomforest = list(num.trees = c(250,500),
                         mtry = c(5,10)),
  bm_pls_pcr = list(method = c("simpls","kernelpls","svdpc")),
  bm_cubist  = list(committees= c(1,5, 15)),
  bm_xgb = list()
)


base_predictors <- names(pars_predictors)

specs <- model_specs(base_predictors,pars_predictors)

# building the ensemble
model <- quickADE(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, ])
}


# 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