STONE: Signal Temporal Logic Neural Network for Time Series Classification

Yan, Ruixuan; Julius, A. Agung; Chang, Maria Hsia; Fokoue, Achille; Ma, Tengfei; Uceda-Sosa, Rosario

doi:10.1109/icdmw53433.2021.00101

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…For the end-user, it is hard to interpret the weights and define their preferences in the temporal logic formalism, so there needs to be an intermediate step to infer the weights from the user. In [31], [32], a parametric extension of WSTL, which we call PWSTL, is used in a time series classification problem, where weights of the formula are learned using neural networks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the WSTL definition of [12], weights are predetermined positive real values. In this work, we use an extension to WSTL that we call Parametric Weighted Signal Temporal Logic (PWSTL) in which some of the weights are unknown parameters and the remaining weights are given constants (cf., [31]). We denote the set of unknown parameters as W and denote PWSTL formulas as ϕ W , where we omit the known weights with slight abuse of notation since for most of the results in the paper W is the entire weight set.…”

Section: B Weighted Signal Temporal Logic (Wstl)mentioning

confidence: 99%

See 1 more Smart Citation

Classification of Driving Behaviors Using STL Formulas: A Comparative Study

Karagulle

Aréchiga²,

DeCastro³

et al. 2022

Lecture Notes in Computer Science

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In this paper, we conduct a preliminary comparative study of classification of longitudinal driving behavior using Signal Temporal Logic (STL) formulas. The goal of the classification problem is to distinguish between different driving styles or vehicles. The results can be used to design and test autonomous vehicle policies. We work on a reallife dataset, the Highway Drone Dataset (HighD). To solve this problem, our first approach starts with a formula template and reduces the classification problem to a Mixed-Integer Linear Program (MILP). Solving MILPs becomes computationally challenging with increasing number of variables and constraints. We propose two improvements to split the classification problem into smaller ones. We prove that these simpler problems are related to the original classification problem in a way that their feasibility imply that of the original. Finally, we compare our MILP formulation with an existing STL-based classification tool, LoTuS, in terms of accuracy and execution time.

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Section: Literature Reviewmentioning

confidence: 99%

Section: B Weighted Signal Temporal Logic (Wstl)mentioning

confidence: 99%