  Time-series data can represent the behaviors of autonomous systems, such as
drones and self-driving cars. The task of binary and multi-class classification
for time-series data has become a prominent area of research. Neural networks
represent a popular approach to classifying data; However, they lack
interpretability, which poses a significant challenge in extracting meaningful
information from them. Signal Temporal Logic (STL) is a formalism that
describes the properties of timed behaviors. We propose a method that combines
all of the above: neural networks that represent STL specifications for
multi-class classification of time-series data. We offer two key contributions:
1) We introduce a notion of margin for multi-class classification, and 2) we
introduce STL-based attributes for enhancing the interpretability of the
results. We evaluate our method on two datasets and compare it with
state-of-the-art baselines.
