Figure 4
From: Forecasting high-dimensional dynamics exploiting suboptimal embeddings
Forecast performance with the Kuramoto–Sivashinsky equations: comparisons of the performance (a) up to 10 steps ahead with the fixed data length (4000) without noise, (b) with different values of the data length, and (c) with different scales of observational noise. We computed the RMSE of the five-steps-ahead forecasts with randomly distributed embedding (RDE), multiview embedding (MVE), state-dependent weighting (SDW), single-best embedding based on the (μ + λ)-ES algorithm (SBE), single-variable embedding (SVE), all-variable embedding (AVE), and the proposed framework. These tests were carried out with 20 datasets generated with different random initial conditions and noise. The median, upper quartile, and lower quartile are shown.
