Confusion matrix for performance of the naïve persistence model
| Observed | |||
|---|---|---|---|
| 0 | 1 | ||
| Predicted | 0 | 13,698/13,814 | 116/13,814 |
| 99.16% | 0.84% | ||
| 1 | 116/586 | 470/586 | |
| 19.8% | 80.2% | ||
| Observed | |||
|---|---|---|---|
| 0 | 1 | ||
| Predicted | 0 | 13,698/13,814 | 116/13,814 |
| 99.16% | 0.84% | ||
| 1 | 116/586 | 470/586 | |
| 19.8% | 80.2% | ||
Note(s): This model develops a forecast using only the lightning state of the previous timestamp. For instance, if there is no lightning at time t, then the model predicts no lightning at t + 1. The wavelet enabled semi-parametric modeling approach outperforms a naïve model in this implementation and indicates this new methodology has explanatory power in the prediction of lightning phenomena
Source(s): Table courtesy of authors
Sharing content requires targeting cookies to be enabled. Please update your cookie preferences to use this feature.