Summary of Generative Adversarial Network with Soft-dynamic Time Warping and Parallel Reconstruction For Energy Time Series Anomaly Detection, by Hardik Prabhu et al.
Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection
by Hardik Prabhu, Jayaraman Valadi, Pandarasamy Arjunan
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a deep learning-based approach for detecting anomalies in energy time series data. A one-dimensional deep convolutional generative adversarial network (DCGAN) is used to identify noise patterns that deviate from typical energy consumption behavior. The method, which combines reconstruction loss and prior probability distributions, outperforms traditional Euclidean distance metrics. Experimental results demonstrate the effectiveness of this approach in identifying anomalous energy consumption patterns in buildings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special kind of computer program to find strange patterns in energy usage data from 15 different buildings. It’s like trying to figure out what makes one building use more or less energy than another. The program, called a DCGAN, looks at tiny pieces of time series data and tries to recreate them. If it can’t recreate them very well, that means there might be something unusual going on with the energy usage. This could help us understand why some buildings are using more energy than others. |
Keywords
* Artificial intelligence * Deep learning * Euclidean distance * Generative adversarial network * Probability * Time series
