open access

https://hdl.handle.net/2115/58420

Evaluation of the predictability of fishing forecasts using information theory

英語

Relative entropy

2014-05

Springer

Fisheries Science

80(3)

427-434

The catch forecast is important for fisheries activities. Previous research has tried to improve forecast accuracy. However the forecast accuracy does not directly correspond to the forecast benefit, and an inaccurate forecast could be more beneficial than accurate one. Herein as part of the forecast utility, predictability was evaluated using information theory. Mutual information (MI) was used as index of predictability. MI denotes a reduction in uncertainty when a forecast is taken into account. Adding this, hit ratio (HR) and relative entropy (R) were used as consistency indices. HR denotes a frequency for which the predicted values are consistent with the actual values, and R denotes the distance of the probability distribution between the actual and forecasted fishing conditions. As an application, the long-term change-ratio forecasts in 1972-2009 (n = 36), short-term change-ratio forecasts (n = 34), and short-term level forecasts (n = 33) in 2004-2009 of Pacific saury Cololabis saira fishery were evaluated. The order of MI, HR, and R varied between these forecasts, indicating that forecast predictability and consistency do not correspond. Monitoring multiple indices would improve forecasting systems.

学術雑誌論文

Accepted Manuscript

The original publication is available at www.springerlink.com

0919-9268

AA10993718