z-logo
open-access-imgOpen Access
A new approach to testing forecast predictive accuracy
Author(s) -
Gilleland Eric,
Roux Gregory
Publication year - 2015
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1485
Subject(s) - dynamic time warping , computer science , test (biology) , data mining , artificial intelligence , machine learning , biology , paleontology
The Diebold–Mariano test for predictive accuracy has been used widely and adapted for economic forecasts, but has not seen much activity in weather forecast verification. The technique is applied to both simulated verification sets as well as weather data at eight stations in Utah, and a loss function based on dynamic time warping (DTW) is used. Results of the simulation experiment show that the DTW technique can be useful if timing errors are the concern. Real test cases demonstrate the difficulty in automating some of the more advanced methods proposed here, but also show the utility in even the most basic test, which is an improvement over similar tests that do not account for temporal and/or contemporaneous correlation.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here