# Deep State-Space Model for Predicting Cryptocurrency Price

arXiv: https://arxiv.org/abs/2311.14731 · HTML: https://mlsanity.com/paper/2311.14731/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Optimal approximation of continuous functions by very deep ReLU networks](https://mlsanity.com/paper/1802.03620/) (2018)
- [Global Convergence of ADMM in Nonconvex Nonsmooth Optimization](https://mlsanity.com/paper/1511.06324/) (2015)
- [Deep Neural Network Approximation Theory](https://mlsanity.com/paper/1901.02220/) (2019)
- [Why Deep Neural Networks for Function Approximation?](https://mlsanity.com/paper/1610.04161/) (2016)
- [Time Series Forecasting Using LSTM Networks: A Symbolic Approach](https://mlsanity.com/paper/2003.05672/) (2020)
- [A deep matrix factorization method for learning attribute representations](https://mlsanity.com/paper/1509.03248/) (2015)
- [A General Analysis of the Convergence of ADMM](https://mlsanity.com/paper/1502.02009/) (2015)
- [Deep Dictionary Learning: A PARametric NETwork Approach](https://mlsanity.com/paper/1803.04022/) (2018)
- [Optimal Function Approximation with Relu Neural Networks](https://mlsanity.com/paper/1909.03731/) (2019)
- [On the Global Linear Convergence of the ADMM with Multi-Block Variables](https://mlsanity.com/paper/1408.4266/) (2014)
- [Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Models](https://mlsanity.com/paper/2306.11652/) (2023)
- [Improved Forecasting of Cryptocurrency Price using Social Signals](https://mlsanity.com/paper/1907.00558/) (2019)
- [Nonnegative matrix factorization with side information for time series recovery and prediction](https://mlsanity.com/paper/1709.06320/) (2017)
- [GraphIT: Iterative reweighted ℓ1 algorithm for sparse graph inference in state-space models](https://mlsanity.com/paper/2303.12569/) (2023)

## Abstract (arXiv)

Our work presents two fundamental contributions. On the application side, we tackle the challenging problem of predicting day-ahead crypto-currency prices. On the methodological side, a new dynamical modeling approach is proposed. Our approach keeps the probabilistic formulation of the state-space model, which provides uncertainty quantification on the estimates, and the function approximation ability of deep neural networks. We call the proposed approach the deep state-space model. The experiments are carried out on established cryptocurrencies (obtained from Yahoo Finance). The goal of the work has been to predict the price for the next day. Benchmarking has been done with both state-of-the-art and classical dynamical modeling techniques. Results show that the proposed approach yields the best overall results in terms of accuracy.