Summary of Deep State Space Recurrent Neural Networks For Time Series Forecasting, by Hugo Inzirillo
Deep State Space Recurrent Neural Networks for Time Series Forecasting
by Hugo Inzirillo
First submitted to arxiv on: 21 Jul 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 delves into various neural network architectures for modeling cryptocurrency market dynamics. Traditional linear models struggle to accurately capture this complex market, whereas Deep Neural Networks (DNNs) have shown promise in time series forecasting. The proposed framework combines econometric state space models with Recurrent Neural Networks (RNNs), specifically utilizing Long Short Term Memory (LSTM), Gated Residual Units (GRU), and Temporal Kolmogorov-Arnold Networks (TKANs). Experimental results indicate that TKANs, inspired by LSTM and Temporal Kolmogorov-Arnold Networks (KANs), yield promising outcomes for modeling cryptocurrency market dynamics. This research contributes to the development of more accurate models for predicting cryptocurrency prices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at different ways to use computer programs called neural networks to understand how the value of cryptocurrencies changes over time. Normally, simple math formulas don’t work well for this because the market is very complicated and unpredictable. But some types of neural networks are better than others at predicting what will happen in the future. The researchers combined ideas from economics with a type of neural network called Recurrent Neural Networks (RNNs) to create a new way to model the cryptocurrency market. They tested different versions of this approach and found that one version, called TKAN, worked particularly well. |
Keywords
» Artificial intelligence » Lstm » Neural network » Time series
