Research Article
Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series
Table 19
For K = 12 , training and testing samples variation for FTLRNN on testing data set for monthly sunspot time series and K = 10 for laser time series.
| Time series | Sunspot time series | Laser time series |
| Training exemplars | Testing exemplars | MSE | NMSE | | MSE | NMSE | |
| 10% | 75% | 0.01187 | 0.32145 | 0.87483 | 0.01506 | 0.45940 | 0.76210 | 20% | 65% | 0.01116 | 0.29307 | 0.84917 | 0.01310 | 0.38332 | 0.80583 | 30% | 55% | 0.00967 | 0.23485 | 0.88221 | 0.00140 | 0.43507 | 0.77113 | 40% | 45% | 0.00749 | 0.17299 | 0.91236 | 0.01221 | 0.48119 | 0.75879 | 50% | 35% | 0.00909 | 0.19003 | 0.91050 | 0.00444 | 0.20709 | 0.89826 | 60% | 25% | 0.01693 | 0.30599 | 0.8550 | 0.00956 | 0.337 | 0.9014 | 70% | 15% | 0.00841 | 0.17893 | 0.92008 | 0.01047 | 0.28229 | 0.87301 | 80% | 05% | 0.00871 | 0.26119 | 0.86836 | 0.02631 | 0.59165 | 0.71077 |
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