Case studies of International Business Machines Corporation IBMM) have been used in different studies to test forecasting accuracy of different approaches.
However, from our result mentioned above, the introduction of the modified G-R law into the forecast model is obviously effective to improve the prediction as far as our tested forecasting period is concerned (seven years at most in Section 7.5).
We found that the accuracy of classification of DLBP vs. CLBP was not very high in the blind test (forecasting ability, 67.24%; sensitivity, 70%), although a higher accuracy was observed for classification of DLBP vs. LDH and LDH vs. N (forecasting abilities, ~90%; sensitivities, >90%).
The accuracy of models for classification of DLBP vs. CLBP was not very high in the blind test (forecasting ability, 67.24%; sensitivity, 70%), although a higher accuracy was observed for classification of DLBP vs. LDH and LDH vs. N (forecasting abilities, ~90%; sensitivities, >90%).
This paper firstly proposes a new approach to test forecasts' accuracy and efficiency under asymmetric loss function by using a unique dataset from surveys conducted by the State Administration of Foreign Exchange in China.
Subsequently, the ship heave time series under four working conditions were used to conduct numerical example, to test forecast performance of the proposed PSR DSRvSVR CAEFOA approach and optimize performance of CAEFOA.
The SRE data is much more suitable than large recurrent event data for experiments of prospective probabilistic forecasts for three reasons: (1) events are objectively qualified and accurate in time; (2) the recurrence intervals are short; and (3) the catalog of events is compiled based on a stable observation network and contains many sequences to test forecasts statistically.
To illustrate the method, they took the example of a 10-year experiment by Rundle et al. (2002), 2003) to predict M ≥ 5 earthquakes in California, and tested forecasts from three models: RI, PI, and the NSHM (a model that comprises seismicity smoothed over distances as large as 60 km, zones of background seismicity, and explicit fault information).
An ensemble approach is also used for generating and testing AQ forecasting models.
The modeling, parameter estimation, model testing and forecasting result analysis were based on the assumption that future prices were related to historical prices and random variables.
