融合情绪分析与双向LSTM，提升比特币等加密货币价格预测精度
FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics
- 用FinBERT提取市场情绪，结合Bi-LSTM捕捉时序特征
- 在BTC/ETH数据集上显著优于传统LSTM与FinBERT-LSTM模型
- 适合关注加密市场波动、需情绪驱动决策的投资者
时间序列预测是金融市场的关键工具，有助于预测资产价格并指导投资决策。在比特币（BTC）和以太坊（ETH）等高波动性加密货币市场中，因市场情绪、技术变革和监管变化引发的价格剧烈波动，使预测变得更加困难。传统方法依赖统计模型，但随着市场复杂性上升，深度学习模型如LSTM、Bi-LSTM及新兴的FinBERT-LSTM逐渐被采用以捕捉复杂模式。本文提出一种混合模型，将双向长短期记忆网络（Bi-LSTM）与FinBERT结合，旨在提升此类资产的价格预测准确性。该方法通过融合先进时序模型与情感分析，弥补了高波动金融市场预测中的关键空白，为投资者和分析师在不确定市场环境中提供有价值的洞察。
原文摘要 · Abstract (English)
Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。