Next-Day Bitcoin Price Forecast Based on Artificial intelligence Methods
- URL: http://arxiv.org/abs/2106.12961v1
- Date: Mon, 21 Jun 2021 04:45:59 GMT
- Title: Next-Day Bitcoin Price Forecast Based on Artificial intelligence Methods
- Authors: Liping Yang
- Abstract summary: This paper proposed a method combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning method called long short-term memory (LSTM) to research the problem of next-day Bitcoin price forecast.
- Score: 6.09170287691728
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, Bitcoin price prediction has attracted the interest of
researchers and investors. However, the accuracy of previous studies is not
well enough. Machine learning and deep learning methods have been proved to
have strong prediction ability in this area. This paper proposed a method
combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning
method called long short-term memory (LSTM) to research the problem of next-day
Bitcoin price forecast.
Related papers
- Using Sentiment and Technical Analysis to Predict Bitcoin with Machine Learning [1.3053649021965603]
This work represents a preliminary study on the importance of sentiment metrics in cryptocurrency forecasting.
We present a novel approach for predicting Bitcoin price by combining the Fear & Greedy Index, a measure of market sentiment, Technical Analysis indicators, and the potential of Machine Learning algorithms.
arXiv Detail & Related papers (2024-10-18T15:13:07Z) - Interplay between Cryptocurrency Transactions and Online Financial
Forums [41.94295877935867]
This study focuses on the study of the interplay between these cryptocurrency forums and fluctuations in cryptocurrency values.
It shows that the activity of Bitcointalk forum keeps a direct relationship with the trend in the values of BTC.
The experiment highlights that forum data can explain specific events in the financial field.
arXiv Detail & Related papers (2023-11-27T16:25:28Z) - A Data-driven Deep Learning Approach for Bitcoin Price Forecasting [10.120972108960425]
We propose a shallow Bidirectional-LSTM (Bi-LSTM) model to forecast bitcoin closing prices in a daily time frame.
We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models.
arXiv Detail & Related papers (2023-10-27T10:35:47Z) - 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) - A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools
Stock Prediction [100.9772316028191]
In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models.
Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation.
arXiv Detail & Related papers (2022-05-01T05:12:22Z) - Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump [39.06710188537909]
This paper focuses on predicting the pump probability of all coins listed in the target exchange before a scheduled pump time.
We conduct a comprehensive study of the latest 709 P&D events organized in Telegram from Jan. 2019 to Jan. 2022.
We develop a novel sequence-based neural network, dubbed SNN, which encodes a channel's P&D event history into a sequence representation.
arXiv Detail & Related papers (2022-04-21T16:34:53Z) - 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) - Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction
with Representation Learning and Temporal Convolutional Network [71.25144476293507]
We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market.
With representation learning, we derived an embedding called Stock2Vec, which gives us insight for the relationship among different stocks.
Our hybrid framework integrates both advantages and achieves better performance on the stock price prediction task than several popular benchmarked models.
arXiv Detail & Related papers (2020-09-29T22:54:30Z) - A Blockchain Transaction Graph based Machine Learning Method for Bitcoin
Price Prediction [8.575998118995216]
Existing bitcoin prediction works mostly on trivial feature engineering.
We propose k-order transaction graph to reveal patterns under different scope.
A novel prediction method is proposed to accept the features and make price prediction, which can take advantage from particular patterns from different history period.
arXiv Detail & Related papers (2020-08-21T20:08:17Z) - Real-Time Prediction of BITCOIN Price using Machine Learning Techniques
and Public Sentiment Analysis [0.0]
The objective of this paper is to determine the predictable price direction of Bitcoin in USD by machine learning techniques and sentiment analysis.
Twitter and Reddit have attracted a great deal of attention from researchers to study public sentiment.
We have applied sentiment analysis and supervised machine learning principles to the extracted tweets from Twitter and Reddit posts.
arXiv Detail & Related papers (2020-06-18T15:40:11Z) - Forecasting Bitcoin closing price series using linear regression and
neural networks models [4.17510581764131]
We study how to forecast daily closing price series of Bitcoin using data prices and volumes of prior days.
We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms.
arXiv Detail & Related papers (2020-01-04T21:04:05Z)
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.