  This work contributes to the development of neural forecasting models with
novel randomization-based learning methods. These methods improve the fitting
abilities of the neural model, in comparison to the standard method, by
generating network parameters in accordance with the data and target function
features. A pattern-based representation of time series makes the proposed
approach useful for forecasting time series with multiple seasonality. In the
simulation study, we evaluate the performance of the proposed models and find
that they can compete in terms of forecasting accuracy with fully-trained
networks. Extremely fast and easy training, simple architecture, ease of
implementation, high accuracy as well as dealing with nonstationarity and
multiple seasonality in time series make the proposed model very attractive for
a wide range of complex time series forecasting problems.
