👉 The Evaluation Fluid (EF) is a critical component in the evaluation of Liquid Time-Constant Networks (LTCs), a type of recurrent neural network (RNN) designed for time-series prediction. EF measures the ability of an LTC to accurately predict the evaluation signal by comparing its output to the true target. It is computed as the ratio of the mean squared error (MSE) between the LTC's predicted evaluation signal and the actual evaluation signal over a sliding window of time. This metric provides a comprehensive assessment of the LTC's performance, capturing both its predictive accuracy and stability over time. A lower EF value indicates better performance, as the LTC consistently produces predictions that closely match the true values.
