A NO-LINEAR HYBRID MODEL FOR MULTI-STEP-AHEAD FORECASTING OF CHAOTIC TIME-SERIES

Forecasting of chaotic time-series has increasingly become a popular and challenging
subject. Many of the forecasting methods proposed in the literature are either inefficient
when applied to multi 'itep-ahead forecasting of chaotic time series as they only perform
one-step-ahead forecasts, or difficult to implement in terms of model complexity. The
motivation to conduct the current study is to develop a more effective, easy-to-use and
practical method for multi-step-ahead forecasting of chaotic time-series. Over the last
decade. the main advances in forecasting are hybrid and ensemble modelling. Theoretical
and empirical studies reported in the literature suggest that one of the best ways
of enhancing forecasting performance is by hybrid modelling. where the models that
constitutes the hybrid model function in a different manner hence capturing disparate
data patterns.