Generalized functional linear model

The generalized functional linear model (GFLM) is an extension of the generalized linear model (GLM) that allows one to regress univariate responses of various types (continuous or discrete) on functional predictors, which are mostly random trajectories generated by a square-integrable stochastic processes. Similarly to GLM, a link function relates the expected value of the response variable to a linear predictor, which in case of GFLM is obtained by forming the scalar product of the random predictor function X {\displaystyle X} with a smooth parameter function β {\displaystyle \beta } .

Source: Wikipedia — Generalized functional linear model (CC BY-SA 4.0)