Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The combination of forecasts is a ll established procedure for improving forecast performance and decreasing the risk of selecting an inferior model out of an existing pool of models. Work in this area mainly focuses on combining several functionally different models, but some publications also deal with combining forecasts with the same functional approach. In the latter case, individual forecasts are generated by diversifying one or more model parameters or, if dealing with hierarchical data, by using forecasts from different levels. This work looks at multi-dimensional data from airline industry, with the aim of improving the forecast of cancellation rates for bookings. Three different methods are employed for the generation of individual forecasts. Forecast combinations are usually implemented in a more or less static structure, either including all available forecasts or trimming a fixed percentage of the worst performing models. For a big number of individual forecasts, this procedure can become inefficient. , a dynamic approach of pooling and trimming is applied to the generated forecasts for airline cancellation data.
['Christiane Lemke', 'Silvia Riedel', 'Bogdan Gabrys']
I am writing a research paper on a idea Dynamic combination of forecasts generated by diversification procedures applied to forecasting of airline cancellations
[{"from": "human", "value": "I am writing a research paper on a idea Dynamic combination of forecasts generated by diversification procedures applied to forecasting of airline cancellations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The combination of forecasts is a ll established procedure for improving forecast performance and decreasing the risk of selecting an inferior model out of an existing pool of models. Work in this area mainly focuses on combining several functionally different models, but some publications also deal with combining forecasts with the same functional approach. In the latter case, individual forecasts are generated by diversifying one or more model parameters or, if dealing with hierarchical data, by using forecasts from different levels. This work looks at multi-dimensional data from airline industry, with the aim of improving the forecast of cancellation rates for bookings. Three different methods are employed for the generation of individual forecasts. Forecast combinations are usually implemented in a more or less static structure, either including all available forecasts or trimming a fixed percentage of the worst performing models. For a big number of individual forecasts, this procedure can become inefficient. , a dynamic approach of pooling and trimming is applied to the generated forecasts for airline cancellation data."}]