Ballistocardiogram (BCG) is a vital sign of ballistic forces generated by each heartbeat. With the advancements in related sensor and computing technologies in recent years, BCG has become far more accessible and thus regained its interest in both research and industry fields. Here we would like to promote the system modelling approach to BCG computing that allows to explore the underlying association between BCG and other physiological signals such as electrocardiogram (ECG). This is in contrast to most of the existing works in the related signal processing domain, which focus on detecting heart rate only. The system modelling approach may eventually improve the clinical significance of the BCG by extracting deeply embedded information. Towards this goal, here we present our preliminary study where we design a Wavelet-based temporal-frequency system model for associating BCG and ECG. To validate the model, we also collect simultaneous BCG and ECG recordings from 4 healthy subjects. We use the system model to build a BCG to ECG predicting algorithm. We demonstrate that this temporal-frequency model and algorithm is far superior, in terms of accuracy, to the naïve method of linear modelling.