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September 5, 2025

Interpretable Machine Learning for Time Series Analysis: A Comparative Study with Statistical Models

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Authors

MAMuhammad Sajid Aqeel AhmadSouth Dakota State University ARAbdul RehmanShifa International Hospital RKRoidar KhanUniversity of Malakand

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Implication

This comparative analysis reveals forecasting accuracy differences in statistical models and machine learning methods, suggesting hybrid approaches may enhance performance.

Key Points

Statistical models like ARIMA showed good interpretability scores but had higher forecasting errors, indicating a reliability trade-off.
Machine learning models such as LSTM outperformed statistical models in accuracy with the lowest RMSE of 43.7, demonstrating their forecasting power under noisy conditions.
The analysis indicates a clear trade-off: greater accuracy in machine learning models comes at the cost of interpretability, with scores ranging from 2-3.
Hybrid approaches may combine the strengths of both methods, potentially improving real-world forecasting applications and addressing the interpretability issue.

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Ahmad et al. (2025) studied this question.

synapsesocial.com/papers/68bb49db6d6d5674bcd004d4 https://doi.org/10.63056/acad.004.03.0681

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Implication

This comparative analysis reveals forecasting accuracy differences in statistical models and machine learning methods, suggesting hybrid approaches may enhance performance.

Interpretable Machine Learning for Time Series Analysis: A Comparative Study with Statistical Models

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Member takes

Implication

Key Points

Cite This Study

Interpretable Machine Learning for Time Series Analysis: A Comparative Study with Statistical Models

Discussion

Member takes

Implication

Key Points

Cite This Study

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