Loading Now

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

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

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