In analog computing, multimode fibers (MMFs) have been explored for their potential in performing computations by exploiting the inherent parallelism of light propagation through different transverse modes. However, traditional approaches using MMFs encounter challenges due to limitations in spatial-domain encoding, which limit computational speed and accuracy. Innovative research seeks to overcome these limitations, by leveraging novel techniques in temporal encoding and employing step-index few-mode fibers (FMFs), and enhance the computational capabilities of analog systems. In this work, we consider a 13 m FMF to implement up to 7-bit header classification at 28.5 Gb/s, when utilizing the information that is included in only one-bit duration. By activating a small number of spatial modes with proper dispersive optical characteristics, we are able to limit the complexity of the spatial transformation and solve such computing tasks efficiently. The selective activation of supported modes is performed by testing varying input displacement conditions between the optical beam encoding information and the FMF input tip. At the output of the fiber, we obtain multiple time series that correspond to different spatial sub-patterns of the output beam overall pattern. Finally, we train a logistic regression classifier that uses samples from these time series, and evaluate its performance in classification tasks. Notably, even in the presence of inter-header interference, our system achieves successful 6-bit header classification - a feat unattainable with conventional step-index MMFs.