Cyclic modulation of sensor parameters can improve sensitivity and selectivity of gas sensors. If the modulated parameter influences the sens environment, several readings can be gained, eventually resulting in a multi-dimensional response which can be analyzed with, e.g., principal component analysis. In certain cases, e.g. temperature modulated gas sensors with different thermal time constants, the length of the used cycles, and, thus, the temporal resolution of the sensors can differ. As a consequence, different sensors can produce datasets with an unequal number of observations which, nevertheless, cover the same interval of time. In this work, we explore three different strategies which enable combination of those datasets in order to retain the maximum amount of information from two sensors when used in parallel. Simulated data show that simple combination of a short cycle with the last complete long cycle can improve correct classification rate by 15 percent points while maintaining the better temporal resolution. On the other hand, performance can be further increased at the expense of temporal resolution by adding either several of the short cycles, or their mean, to a long cycle, effectively reducing noise. The proposed combination strategies and their dependence on preprocessing are validated with a real dataset of two gas sensors. Overall, and taking into account differences in data performance for simulated and real data is observed.