Summary of Multi-variable Adversarial Time-series Forecast Model, by Xiaoqiao Chen
Multi-variable Adversarial Time-Series Forecast Model
by Xiaoqiao Chen
First submitted to arxiv on: 2 Jun 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 The paper proposes a new framework for short-term industrial enterprise power system forecasting, which combines Long Short-term Memory (LSTM) models with an adversarial process. The multi-variable adversarial time-series forecasting model can forecast multiple variables simultaneously, including continuous and categorical ones, while considering their interdependencies. This approach achieves better prediction accuracy compared to single-variable forecasts. The framework is evaluated qualitatively and quantitatively using real-world data from several large industrial enterprises, demonstrating its potential for improving power system protection and load control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of predicting what will happen in a power grid has been developed. This method combines two types of models: one that looks at the past to predict the future, and another that tries to trick the first model into making mistakes. By combining these two models, we can make more accurate predictions about many different things happening in the power grid at once. This is important because it can help prevent problems in the power grid, such as too much or too little electricity being used. The new method was tested with real data from several big factories and showed that it works well. |
Keywords
» Artificial intelligence » Lstm » Time series
