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Unsupervised Anomaly Detection for Intermittent Sequences Based on Multi-Granularity Abnormal Pattern Mining.

Lilin Fan¹, Jiahu Zhang¹, Wentao Mao¹

¹College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.

Entropy (Basel, Switzerland)

|January 21, 2023

Summary

This summary is machine-generated.

Manufacturing inventory strategies are improved by a new method for detecting anomalies in intermittent after-sale parts demand data. This approach accurately identifies abnormal fluctuations, enhancing demand forecasting and inventory management.

Keywords:

after-sale parts management anomaly detection intermittent sequence safety stock unsupervised learning

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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

Area of Science:

Industrial Engineering
Data Science
Operations Research

Background:

Abnormal changes in after-sale parts demand data disrupt manufacturing inventory strategies.
Intermittent and small-scale demand sequences pose challenges for traditional anomaly detection algorithms.

Purpose of the Study:

To propose an unsupervised anomaly detection method for intermittent time series in manufacturing demand data.
To enhance the accuracy of identifying anomalies in small-sample, intermittent demand sequences.

Main Methods:

Constructed an abnormal fluctuation similarity matrix using squared coefficient of variation and maximum information coefficient.
Employed agglomerative hierarchical clustering for adaptive screening of abnormal fluctuation sequences.
Utilized a support vector data description model with demand change and interval features for micro-granularity anomaly detection.

Main Results:

The proposed method effectively identifies abnormal fluctuation positions in intermittent, small-sample sequences.
Demonstrated superior detection results compared to existing representative anomaly detection methods.
Validated through comparative experiments on actual after-sale parts demand data from two large manufacturing enterprises.

Conclusions:

The developed unsupervised anomaly detection method addresses the limitations of current algorithms for intermittent time series.
This approach offers a robust solution for improving inventory strategies in manufacturing by accurately detecting demand anomalies.