We seek to evaluate the accuracy of computational intelligence (CI) methods in time series forecasting through the 1st Neural Network Forecasting Grand Competition (NNGC), extending the earlier NN3 & NN5 competitions unto a new set of data with multiple time frequencies.
The objective is to forecast one, two or more datasets of a selection of 6 datasets (each containing 11 time series) of transportation data (e.g. airline passenger, car travel, port shipments etc.) as accurately as possible, using methods from computational intelligence and applying a consistent methodology.
Each dataset consists of 11 time series with different time frequencies, including yearly, quarterly, monthly, weekly, daily and hourly transportation data.
The session will present the results on the NNGC and also welcomes participants to present research from earlier NN3 and NN5 competitions.