  In this work, we propose an ensemble forecasting approach based on randomized
neural networks. Improved randomized learning streamlines the fitting abilities
of individual learners by generating network parameters in accordance with the
data and target function features. A pattern-based representation of time
series makes the proposed approach suitable for forecasting time series with
multiple seasonality. We propose six strategies for controlling the diversity
of ensemble members. Case studies conducted on four real-world forecasting
problems verified the effectiveness and superior performance of the proposed
ensemble forecasting approach. It outperformed statistical models as well as
state-of-the-art machine learning models in terms of forecasting accuracy. The
proposed approach has several advantages: fast and easy training, simple
architecture, ease of implementation, high accuracy and the ability to deal
with nonstationarity and multiple seasonality in time series.
