# Deep State Space Recurrent Neural Networks for Time Series Forecasting

arXiv: https://arxiv.org/abs/2407.15236 · HTML: https://mlsanity.com/paper/2407.15236/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 21 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [A Simple Way to Initialize Recurrent Networks of Rectified Linear Units](https://mlsanity.com/paper/1504.00941/) (2015)
- [TKAN: Temporal Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2405.07344/) (2024)
- [A Temporal Kolmogorov-Arnold Transformer for Time Series Forecasting](https://mlsanity.com/paper/2406.02486/) (2024)
- [Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments](https://mlsanity.com/paper/2006.10119/) (2020)
- [The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy](https://mlsanity.com/paper/1408.1494/) (2014)
- [Dimensionality reduction for prediction: Application to Bitcoin and Ethereum](https://mlsanity.com/paper/2112.15036/) (2021)

## Built on by (most cited first, 6 of 6)

- [Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2407.17611/) (2024)
- [A Gated Residual Kolmogorov-Arnold Networks for Mixtures of Experts](https://mlsanity.com/paper/2409.15161/) (2024)
- [KAN we improve on HEP classification tasks? Kolmogorov-Arnold Networks applied to an LHC physics example](https://mlsanity.com/paper/2408.02743/) (2024)
- [A Temporal Linear Network for Time Series Forecasting](https://mlsanity.com/paper/2410.21448/) (2024)
- [HiPPO-KAN: Efficient KAN Model for Time Series Analysis](https://mlsanity.com/paper/2410.14939/) (2024)
- [STAN: Smooth Transition Autoregressive Networks](https://mlsanity.com/paper/2501.18699/) (2025)

## Abstract (arXiv)

We explore various neural network architectures for modeling the dynamics of the cryptocurrency market. Traditional linear models often fall short in accurately capturing the unique and complex dynamics of this market. In contrast, Deep Neural Networks (DNNs) have demonstrated considerable proficiency in time series forecasting. This papers introduces novel neural network framework that blend the principles of econometric state space models with the dynamic capabilities of Recurrent Neural Networks (RNNs). We propose state space models using Long Short Term Memory (LSTM), Gated Residual Units (GRU) and Temporal Kolmogorov-Arnold Networks (TKANs). According to the results, TKANs, inspired by Kolmogorov-Arnold Networks (KANs) and LSTM, demonstrate promising outcomes.