In general, stICA finds a linear decomposition, by maximizing the degree of independence over space as well as over time, without necessarily producing independence in either space or time.
In spatiotemporal ICA (stICA), it is trying to find the decomposition [??] = [SAP.sup.T], where S is an m x k matrix with a set of k statistically independent spatial signals, P is an n x k matrix of k mutually independent temporal signals, and [LAMBDA] is a diagonal scaling matrix and is required to ensure that S and P have amplitudes appropriate to their respective cdfs S and r.
Then, the three different ICA algorithms including tICA, sICA, and stICA are used in the scaled data to estimate ICs.
Using the similar process, the sICA-SVR model uses sICA algorithm to generate spatial ICs (i.e., s_ICs); the t-stICA-SVR model and s-stICA-SVR model utilize stICA algorithm to respectively estimate temporal ICs (i.e., t-st_ICs) and spatial ICs (i.e., s-st_ICs).
Since the t-stICA-SVR and s-stICA-SVR can provide better forecasting results than the tICA-SVR and sICA-SVR models, it indicates that stICA algorithm can estimate more effective ICs and improve sales forecasting performance for IT chain store.