mlsanityData to 7 Oct 2026

Deep State-Space Model for Predicting Cryptocurrency Price

arXiv 2311.14731 ↗First posted 21 Nov 2023q-fin.ST cs.LG stat.APcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 62 references that are in the map. First posted on arXiv 21 Nov 2023.

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17 of 62 references.

References · 17

  1. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  2. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  3. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  4. 2018Optimal approximation of continuous functions by very deep ReLU networkscited by 165
  5. 2015Global Convergence of ADMM in Nonconvex Nonsmooth Optimizationcited by 98
  6. 2019Deep Neural Network Approximation Theorycited by 92
  7. 2016Why Deep Neural Networks for Function Approximation?cited by 82
  8. 2020Time Series Forecasting Using LSTM Networks: A Symbolic Approachcited by 25
  9. 2015A deep matrix factorization method for learning attribute representationscited by 24
  10. 2015A General Analysis of the Convergence of ADMMcited by 22
  11. 2018Deep Dictionary Learning: A PARametric NETwork Approachcited by 12
  12. 2019Optimal Function Approximation with Relu Neural Networkscited by 9
5 more
  1. 2014On the Global Linear Convergence of the ADMM with Multi-Block Variablescited by 6
  2. 2023Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Modelscited by 4
  3. 2019Improved Forecasting of Cryptocurrency Price using Social Signalscited by 3
  4. 2017Nonnegative matrix factorization with side information for time series recovery and predictioncited by 2
  5. 2023GraphIT: Iterative reweighted ℓ1 algorithm for sparse graph inference in state-space modelscited by 2

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