TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting

• URL: http://arxiv.org/abs/2409.15950v1
• Date: Tue, 24 Sep 2024 10:24:53 GMT
• Title: TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting
• Authors: Hongnan Ma, Kevin McAreavey, Weiru Liu,
• Abstract summary: This paper presents a framework - TSFeatLIME, extending TSLIME. TSFeatLIME integrates an auxiliary feature into the surrogate model and considers the pairwise Euclidean distances between the queried time series and the generated samples. Results show that the surrogate model under the TSFeatLIME framework is able to better simulate the behaviour of the black-box considering distance, without sacrificing accuracy.
• Score: 1.9314780151274307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series forecasting, while vital in various applications, often employs complex models that are difficult for humans to understand. Effective explainable AI techniques are crucial to bridging the gap between model predictions and user understanding. This paper presents a framework - TSFeatLIME, extending TSLIME, tailored specifically for explaining univariate time series forecasting. TSFeatLIME integrates an auxiliary feature into the surrogate model and considers the pairwise Euclidean distances between the queried time series and the generated samples to improve the fidelity of the surrogate models. However, the usefulness of such explanations for human beings remains an open question. We address this by conducting a user study with 160 participants through two interactive interfaces, aiming to measure how individuals from different backgrounds can simulate or predict model output changes in the treatment group and control group. Our results show that the surrogate model under the TSFeatLIME framework is able to better simulate the behaviour of the black-box considering distance, without sacrificing accuracy. In addition, the user study suggests that the explanations were significantly more effective for participants without a computer science background.

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