Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators

• URL: http://arxiv.org/abs/2403.03606v1
• Date: Wed, 6 Mar 2024 10:53:12 GMT
• Title: Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators
• Authors: Mohammad Ali Labbaf Khaniki, Mohammad Manthouri
• Abstract summary: methodology integrates the use of technical indicators, a Performer neural network, and BiLSTM. The proposed method has been applied to the hourly and daily timeframes of the major cryptocurrencies.
• Score: 0.5439020425819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study presents an innovative approach for predicting cryptocurrency time series, specifically focusing on Bitcoin, Ethereum, and Litecoin. The methodology integrates the use of technical indicators, a Performer neural network, and BiLSTM (Bidirectional Long Short-Term Memory) to capture temporal dynamics and extract significant features from raw cryptocurrency data. The application of technical indicators, such facilitates the extraction of intricate patterns, momentum, volatility, and trends. The Performer neural network, employing Fast Attention Via positive Orthogonal Random features (FAVOR+), has demonstrated superior computational efficiency and scalability compared to the traditional Multi-head attention mechanism in Transformer models. Additionally, the integration of BiLSTM in the feedforward network enhances the model's capacity to capture temporal dynamics in the data, processing it in both forward and backward directions. This is particularly advantageous for time series data where past and future data points can influence the current state. The proposed method has been applied to the hourly and daily timeframes of the major cryptocurrencies and its performance has been benchmarked against other methods documented in the literature. The results underscore the potential of the proposed method to outperform existing models, marking a significant progression in the field of cryptocurrency price prediction.

Related papers

• Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable Framework for Transaction Anomaly Detection in Ethereum Networks [2.795656498870966]
  This study presents a fusion of Graph Convolutional Networks (GCNs) with Temporal Random Walks (TRW) Our approach, unlike traditional GCNs, leverages the strengths of TRW to discern complex temporal sequences in transactions. Preliminary evaluations demonstrate that our TRW-GCN framework substantially advances performance metrics over conventional GCNs.
  arXiv  Detail & Related papers  (2023-09-29T21:08:21Z)
• Stock Price Prediction using Dynamic Neural Networks [0.0]
  This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices. Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, and seemingly random data.
  arXiv  Detail & Related papers  (2023-06-18T20:06:44Z)
• Stock Trend Prediction: A Semantic Segmentation Approach [3.718476964451589]
  We present a novel approach to predict long-term daily stock price change trends with fully 2D-convolutional encoder-decoders. Our hierarchical structure of CNNs makes it capable of capturing both long and short-term temporal relationships effectively.
  arXiv  Detail & Related papers  (2023-03-09T01:29:09Z)
• DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [53.37679435230207]
  We propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data.
  arXiv  Detail & Related papers  (2022-09-23T16:13:47Z)
• Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification [83.23129279407271]
  We propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed. This knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data.
  arXiv  Detail & Related papers  (2022-07-23T18:54:10Z)
• 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)
• Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction [77.57991021445959]
  We propose a neural layer based on the ideas of temporal attention and multi-head attention to extend the capability of the underlying neural network. The effectiveness of our approach is validated using large-scale limit-order book market data.
  arXiv  Detail & Related papers  (2022-01-14T14:02:19Z)
• 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)
• Spatiotemporal convolutional network for time-series prediction and causal inference [21.895413699349966]
  A neural network computing framework, i.N.N., was developed to efficiently and accurately render a multistep-ahead prediction of a time series. The framework has great potential in practical applications in artificial intelligence (AI) or machine learning fields.
  arXiv  Detail & Related papers  (2021-07-03T06:20:43Z)
• 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)
• Liquid Time-constant Networks [117.57116214802504]
  We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations.
  arXiv  Detail & Related papers  (2020-06-08T09:53:35Z)

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.