Regime Detection on Synthetic Time Series SVAR Gauss
0.99NMIFlowMSM
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| FlowMSM2026.06 | 0.99 | 1 | 100 | 100 | |
| iMSM (Neural)Model Variant=Neural2026.06 | 0.95 | 0.98 | 99 | 99 | |
| R-PCMCI2026.06 | 0.95 | 0.95 | 99 | 98 | |
| MCD2026.06 | 0.94 | 0.92 | 92 | 94 | |
| FlowMSM2026.06 | 0.9 | 0.91 | 95 | 95 | |
| iMSM (Neural)2026.06 | 0.89 | 0.9 | 93 | 94 | |
| iMSM (Poly)Model Variant=Poly2026.06 | 0.87 | 0.93 | 98 | 97 | |
| iMSM (Poly)2026.06 | 0.79 | 0.84 | 90 | 91 | |
| CASTOR2026.06 | 0.56 | 0.56 | 97 | 96 | |
| FANTOM2026.06 | 0.32 | 0.13 | 40 | 27 | |
| R-PCMCI2026.06 | 0.27 | 0.25 | 58 | 63 | |
| CASTOR2026.06 | 0.2 | 0.17 | 52 | 60 | |
| FANTOM2026.06 | 0 | 0 | 25 | 14 |