{ "experiment": "Liquid time-constant irregular forecasting", "learned_time_constants": { "minimum": 0.12157730013132095, "mean": 0.45884114503860474, "maximum": 0.7155194282531738 }, "results": { "liquid_time_constant": { "parameters": 1887, "best_step": 2500, "normal_gaps": { "rmse": 0.08929167687892914, "mae": 0.03883583843708038, "examples": 2000, "sequence_length": 128, "delta_time_range": [ 0.02, 0.12 ] }, "unseen_large_gaps": { "rmse": 1.5136159658432007, "mae": 1.1862934827804565, "examples": 2000, "sequence_length": 128, "delta_time_range": [ 0.12, 0.4 ] } }, "matched_gru": { "parameters": 1887, "best_step": 2500, "normal_gaps": { "rmse": 0.08689922839403152, "mae": 0.040582284331321716, "examples": 2000, "sequence_length": 128, "delta_time_range": [ 0.02, 0.12 ] }, "unseen_large_gaps": { "rmse": 0.27418622374534607, "mae": 0.21493969857692719, "examples": 2000, "sequence_length": 128, "delta_time_range": [ 0.12, 0.4 ] } }, "matched_rnn": { "parameters": 1887, "best_step": 2500, "normal_gaps": { "rmse": 0.08811825513839722, "mae": 0.04227612167596817, "examples": 2000, "sequence_length": 128, "delta_time_range": [ 0.02, 0.12 ] }, "unseen_large_gaps": { "rmse": 0.2811814546585083, "mae": 0.21727129817008972, "examples": 2000, "sequence_length": 128, "delta_time_range": [ 0.12, 0.4 ] } } } }