no code implementations • 20 Jun 2021 • Gerald M Pao, Cameron Smith, Joseph Park, Keichi Takahashi, Wassapon Watanakeesuntorn, Hiroaki Natsukawa, Sreekanth H Chalasani, Tom Lorimer, Ryousei Takano, Nuttida Rungratsameetaweemana, George Sugihara
Thus, as a final validation of how well GMN captures essential dynamic information, we show that the artificially generated time series can be used as a training set to predict out-of-sample observed fly locomotion, as well as brain activity in out of sample withheld data not used in model building.
1 code implementation • 2 Dec 2020 • Wassapon Watanakeesuntorn, Keichi Takahashi, Kohei Ichikawa, Joseph Park, George Sugihara, Ryousei Takano, Jason Haga, Gerald M. Pao
Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference framework.