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Towards efficient deep autoencoders for multivariate time series anomaly detection

arXiv 2403.02429 ↗First posted 4 Mar 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Multivariate time series anomaly detection is a crucial problem in many industrial and research applications. Timely detection of anomalies allows, for instance, to prevent defects in manufacturing processes and failures in cyberphysical systems. Deep learning methods are preferred among others for their accuracy and robustness for the analysis of complex multivariate data. However, a key aspect is being able to extract predictions in a timely manner, to accommodate real-time requirements in different applications. In the case of deep learning models, model reduction is extremely important to achieve optimal results in real-time systems with limited time and memory constraints. In this paper, we address this issue by proposing a novel compression method for deep autoencoders that involves three key factors. First, pruning reduces the number of weights, while preventing catastrophic drops in accuracy by means of a fast search process that identifies high sparsity levels. Second, linear and non-linear quantization reduces model complexity by reducing the number of bits for every single weight. The combined contribution of these three aspects allow the model size to be reduced, by removing a subset of the weights (pruning), and decreasing their bit-width (quantization). As a result, the compressed model is faster and easier to adopt in highly constrained hardware environments. Experiments performed on popular multivariate anomaly detection benchmarks, show that our method is capable of achieving significant model compression ratio (between 80% and 95%) without a significant reduction in the anomaly detection performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 4 Mar 2024.

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References · 18

  1. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
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  3. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  4. 2020Movement Pruning: Adaptive Sparsity by Fine-Tuningcited by 398
  5. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  6. 2016EIE: Efficient Inference Engine on Compressed Deep Neural Networkcited by 291
  7. 2017Variational Dropout Sparsifies Deep Neural Networkscited by 265
  8. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 213
  9. 2015Fixed Point Quantization of Deep Convolutional Networkscited by 141
  10. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  11. 2019Stabilizing the Lottery Ticket Hypothesiscited by 36
  12. 2018One-Class Adversarial Nets for Fraud Detectioncited by 33
6 more
  1. 2017Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streamscited by 19
  2. 2021Ensemble neuroevolution based approach for multivariate time series anomaly detectioncited by 8
  3. 2016Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networkscited by 6
  4. 2020Retrain or not retrain? -- efficient pruning methods of deep CNN networkscited by 6
  5. 2021Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reductioncited by 3
  6. 2023AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detectioncited by 3

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