Summary of Comprehensive Forecasting-based Analysis Of Hybrid and Stacked Stateful/ Stateless Models, by Swayamjit Saha
Comprehensive Forecasting-Based Analysis of Hybrid and Stacked Stateful/ Stateless Models
by Swayamjit Saha
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 abstract discusses the prediction of short-term wind speed for airport sites near two campuses of Mississippi State University using four deep recurrent neural networks (RNNs). The RNNs are Stacked Stateless LSTM, Stacked Stateless GRU, Stacked Stateful LSTM, and Stacked Stateful GRU. The paper analyzes the performance of these models, describing their architectures and RMSE values. It also discusses time and space complexities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wind speed is a powerful source of renewable energy that can replace non-renewable resources to produce electricity. This clean and infinite resource does not harm the environment during production. However, predicting wind speed requires special planning to avoid wasting labor and money setting up systems. This paper predicts short-term wind speed for airport sites near two campuses using four RNNs: Stacked Stateless LSTM, Stacked Stateless GRU, Stacked Stateful LSTM, and Stacked Stateful GRU. It compares the models’ performance, discussing their architectures and RMSE values. |
Keywords
» Artificial intelligence » Lstm
