Summary of Dssrnn: Decomposition-enhanced State-space Recurrent Neural Network For Time-series Analysis, by Ahmad Mohammadshirazi et al.
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series Analysis
by Ahmad Mohammadshirazi, Ali Nosratifiroozsalari, Rajiv Ramnath
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Decomposition State-Space Recurrent Neural Network (DSSRNN) framework addresses the challenges of time series forecasting by combining decomposition analysis, state-space models, and physics-based equations. This novel approach outperforms state-of-the-art transformer-based architectures in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE) on indoor air quality datasets, specifically CO2 concentration prediction across various forecasting horizons. The DSSRNN model exhibits superior computational efficiency compared to more complex models, achieving a balance between performance and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new framework for time series forecasting called the Decomposition State-Space Recurrent Neural Network (DSSRNN). This framework is designed for both long-term and short-term forecasting and combines decomposition analysis with state-space models and physics-based equations. The results show that DSSRNN consistently outperforms other models in terms of accuracy, and it also has better computational efficiency. |
Keywords
» Artificial intelligence » Mae » Mse » Neural network » Time series » Transformer
