CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction

• URL: http://arxiv.org/abs/2501.01010v1
• Date: Thu, 02 Jan 2025 02:16:56 GMT
• Title: CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction
• Authors: Mohammad Shahab Sepehri, Asal Mehradfar, Mahdi Soltanolkotabi, Salman Avestimehr,
• Abstract summary: We propose CryptoMamba, a novel Mamba-based State Space Model (SSM) architecture designed to capture long-range dependencies in financial time-series data. Our experiments show that CryptoMamba not only provides more accurate predictions but also offers enhanced generalizability across different market conditions. Our findings signal a huge advantage for SSMs in stock and cryptocurrency price forecasting tasks.
• Score: 28.15955243872829
• License:
• Abstract: Predicting Bitcoin price remains a challenging problem due to the high volatility and complex non-linear dynamics of cryptocurrency markets. Traditional time-series models, such as ARIMA and GARCH, and recurrent neural networks, like LSTMs, have been widely applied to this task but struggle to capture the regime shifts and long-range dependencies inherent in the data. In this work, we propose CryptoMamba, a novel Mamba-based State Space Model (SSM) architecture designed to effectively capture long-range dependencies in financial time-series data. Our experiments show that CryptoMamba not only provides more accurate predictions but also offers enhanced generalizability across different market conditions, surpassing the limitations of previous models. Coupled with trading algorithms for real-world scenarios, CryptoMamba demonstrates its practical utility by translating accurate forecasts into financial outcomes. Our findings signal a huge advantage for SSMs in stock and cryptocurrency price forecasting tasks.

Related papers

• Comparative Study of Bitcoin Price Prediction [0.0]
  We employ five-fold cross-validation to enhance generalization and utilize L2 regularization to reduce overfitting and noise. Our study demonstrates that the GRUs models offer better accuracy than LSTMs model for predicting Bitcoin's price.
  arXiv  Detail & Related papers  (2024-05-13T18:10:34Z)
• Hawkes-based cryptocurrency forecasting via Limit Order Book data [1.6236898718152877]
  We present a novel prediction algorithm using limit order book (LOB) data rooted in the Hawkes model. Our approach offers a precise forecast of return signs by leveraging predictions of future financial interactions. The efficacy of our approach is validated through Monte Carlo simulations across 50 scenarios.
  arXiv  Detail & Related papers  (2023-12-21T16:31:07Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models [5.88864611435337]
  We propose a deep learning Synthesizer Transformer model for forecasting volatility. Our results show that the model outperforms existing state-of-the-art models. Our findings underscore that the proposed method is a useful tool for forecasting extreme volatility movements in the Bitcoin market.
  arXiv  Detail & Related papers  (2022-10-06T05:44:29Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Bilinear Input Normalization for Neural Networks in Financial Forecasting [101.89872650510074]
  We propose a novel data-driven normalization method for deep neural networks that handle high-frequency financial time-series. The proposed normalization scheme takes into account the bimodal characteristic of financial time-series. Our experiments, conducted with state-of-the-arts neural networks and high-frequency data, show significant improvements over other normalization techniques.
  arXiv  Detail & Related papers  (2021-09-01T07:52:03Z)
• Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting [93.73198973454944]
  Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.
  arXiv  Detail & Related papers  (2021-07-05T10:15:23Z)
• MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price [0.11470070927586015]
  We propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. We performed experiments to predict the daily closing price of Bitcoin (USD), and the experimental results show that MRC-LSTM significantly outperforms a variety of other network structures.
  arXiv  Detail & Related papers  (2021-05-03T09:32:23Z)
• On Technical Trading and Social Media Indicators in Cryptocurrencies' Price Classification Through Deep Learning [7.7172142175424066]
  This work aims to analyse the predictability of price movements of cryptocurrencies on both hourly and daily data observed from January 2017 to January 2021. For our experiments, we used three sets of features: technical, trading and social media indicators, considering a restricted model of only technical indicators and an unrestricted model with technical, trading and social media indicators. The study shows that, based on the hourly frequency results, the unrestricted model outperforms the restricted one.
  arXiv  Detail & Related papers  (2021-02-13T13:18:36Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.