  Time series forecasting is a challenging problem particularly when a time
series expresses multiple seasonality, nonlinear trend and varying variance. In
this work, to forecast complex time series, we propose ensemble learning which
is based on randomized neural networks, and boosted in three ways. These
comprise ensemble learning based on residuals, corrected targets and opposed
response. The latter two methods are employed to ensure similar forecasting
tasks are solved by all ensemble members, which justifies the use of exactly
the same base models at all stages of ensembling. Unification of the tasks for
all members simplifies ensemble learning and leads to increased forecasting
accuracy. This was confirmed in an experimental study involving forecasting
time series with triple seasonality, in which we compare our three variants of
ensemble boosting. The strong points of the proposed ensembles based on RandNNs
are extremely rapid training and pattern-based time series representation,
which extracts relevant information from time series.
