TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models

Udo Schlegel; Duy Lam Vo; Daniel A. Keim; Daniel Seebacher

doi:10.1007/978-3-030-93736-2_1

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TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models

Udo Schlegel Duy Lam Vo Daniel A. Keim Daniel Seebacher

Year: 2021 Communications in computer and information science Pages: 5-14 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-93736-2_1

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Keywords:

Computer science Series (stratigraphy) Time series Artificial intelligence Debugging Segmentation Task (project management) Machine learning Data mining Engineering

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Topics

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

Explainable Artificial Intelligence (XAI)

Physical Sciences → Computer Science → Artificial Intelligence

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models

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Citation History

Topics

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