A powerful way to expand the time and frequency range of material properties is through a method called time-temperature superposition (TTS). Traditionally, TTS has been applied to the dynamical mechanical and flow properties of thermo-rheologically simple materials, where a well-defined master curve can be objectively and accurately obtained by appropriate shifts of curves at different temperatures. However, TTS analysis can also be useful in many other situations where there is scatter in the data and where the principle holds only approximately. In such cases, shifting curves can become a subjective exercise and can often lead to significant errors in the long-term prediction. This mandates the need for an objective method of determining TTS shifts. Here, we adopt a method based on minimizing the "arc length" of the master curve, which is designed to work in situations where there is overlapping data at successive temperatures. We examine the accuracy of the method as a function of increasing noise in the data, and explore the effectiveness of data smoothing prior to TTS shifting. We validate the method using existing experimental data on the creep strain of an aramid fiber and the powder coarsening of an energetic material.