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ResearchFormerly WizWand

Time-series prediction on ETT

0.0263RMSEGreedy
0.0088430.126360.2438770.361393Sep 15, 2025Oct 23, 2025Dec 1, 2025Jan 9, 2026Feb 17, 2026Mar 28, 2026May 6, 2026
Updated 6mo ago

Evaluation results

MethodLinks
GreedyOptimization Strategy=Greedy, Number of agents=100, Model Architecture=RFN2026.050.0263
GreedyOptimization Strategy=Greedy, Number of agents=500, Model Architecture=RFN2026.050.0263
NashNumber of agents=5002026.050.0263
GreedyNumber of agents=1002026.050.0264
GreedyNumber of agents=5002026.050.0264
GreedyNumber of agents=252026.050.0264
NashNumber of agents=1002026.050.0264
NashNumber of agents=252026.050.0264
NashOptimization Strategy=Decentralized (Nash), Number of agents=25, Model Architecture=RFN2026.050.0265
NashOptimization Strategy=Decentralized (Nash), Number of agents=500, Model Architecture=RFN2026.050.0265
GreedyOptimization Strategy=Greedy, Number of agents=25, Model Architecture=RFN2026.050.0265
NashOptimization Strategy=Decentralized (Nash), Number of agents=100, Model Architecture=RFN2026.050.0266
NashModel Type=ESN, Agent Pool Size=1002026.050.0327
GreedyModel Type=ESN, Agent Pool Size=1002026.050.0417
GreedyModel Type=ESN, Agent Pool Size=5002026.050.0503
NashModel Type=RFN, Agent Pool Size=1002026.050.0572
NashModel Type=ESN, Agent Pool Size=5002026.050.0595
NashModel Type=RFN, Agent Pool Size=5002026.050.0945
GreedyModel Type=RFN, Agent Pool Size=1002026.050.1131
GreedyModel Type=RFN, Agent Pool Size=5002026.050.1774
GLMY OptimizedInitialization approach=Small-world2025.090.1927
GLMY OptimizedInitialization approach=Random2025.090.1961
Random RewiringInitialization approach=Small-world2025.090.1973
GLMY OptimizedInitialization approach=Scale-free2025.090.2017
Random RewiringInitialization approach=Random2025.090.3071
Random RewiringInitialization approach=Scale-free2025.090.3465
OriginInitialization approach=Small-world2025.090.4438
OriginInitialization approach=Random2025.090.4519
OriginInitialization approach=Scale-free2025.090.4615