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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.02770 |
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| _version_ | 1866914964077281280 |
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| author | Silva, P. H. O. Cerqueira, A. S. Nepomuceno, E. G. |
| author_facet | Silva, P. H. O. Cerqueira, A. S. Nepomuceno, E. G. |
| contents | The classification of time series is essential for extracting meaningful insights and aiding decision-making in engineering domains. Parametric modeling techniques like NARX are invaluable for comprehending intricate processes, such as environmental time series, owing to their easily interpretable and transparent structures. This article introduces a classification algorithm, Logistic-NARX Multinomial, which merges the NARX methodology with logistic regression. This approach not only produces interpretable models but also effectively tackles challenges associated with multiclass classification. Furthermore, this study introduces an innovative methodology tailored for the railway sector, offering a tool by employing NARX models to interpret the multitude of features derived from onboard sensors. This solution provides profound insights through feature importance analysis, enabling informed decision-making regarding safety and maintenance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02770 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Insightful Railway Track Evaluation: Leveraging NARX Feature Interpretation Silva, P. H. O. Cerqueira, A. S. Nepomuceno, E. G. Signal Processing Machine Learning The classification of time series is essential for extracting meaningful insights and aiding decision-making in engineering domains. Parametric modeling techniques like NARX are invaluable for comprehending intricate processes, such as environmental time series, owing to their easily interpretable and transparent structures. This article introduces a classification algorithm, Logistic-NARX Multinomial, which merges the NARX methodology with logistic regression. This approach not only produces interpretable models but also effectively tackles challenges associated with multiclass classification. Furthermore, this study introduces an innovative methodology tailored for the railway sector, offering a tool by employing NARX models to interpret the multitude of features derived from onboard sensors. This solution provides profound insights through feature importance analysis, enabling informed decision-making regarding safety and maintenance. |
| title | Insightful Railway Track Evaluation: Leveraging NARX Feature Interpretation |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2410.02770 |