A method of transfer function-noise (TFN) modeling is presented that operates in continuous time and uses a predefined family of impulse response (IR) functions. The resulting class of models is referred to as predefined IR function in continuous time (PIRFICT). It provides a useful tool for standardized analysis of time series, as it can be calibrated using irregularly spaced data and does not require a model identification phase prior to calibration. In section 2, the discrete Box-Jenkins (BJ) model is presented and transformed into continuous time to obtain the PIRFICT model. The discrete transfer function of a BJ model, which is made up of a variable number of parameters, is replaced by a simple analytical expression that defines the IR function. From the IR function, block response functions are derived that enable the model to handle irregularly spaced data. In the example application, the parameter estimates and performance of the BJ and PIRFICT model are compared using a data set of 15 piezometers and a simulated series. It was found that the estimated transfer and BR functions of both models follow the same general pattern, although the BJ transfer functions are partly irregular. The performance of both models proves to be highly comparable for all piezometers.