用情感条件生成模型提升动荡市场中的时序预测能力
Beyond Sequential Prediction: Learning Financial Market Dynamics in Volatile and Non-Stationary Environments through Sentiment-Conditioned Generative Modelling
- 融合GAN与NLP，用文本情绪调节数值序列建模
- 在非平稳环境中实现更鲁棒的金融时序预测
- 适合关注市场动态与文本信息融合的研究者
在非平稳且复杂的环境下进行时间序列预测是机器学习中的挑战性任务，尤其当存在异构的数值与文本数据时。传统统计模型如自回归积分滑动平均（ARIMA）依赖线性与平稳性假设，而循环神经网络如长短期记忆（LSTM）在高度波动场景下未必能准确刻画分布特性。本文提出一种混合模型，结合生成对抗网络（GANs）与基于自然语言处理（NLP）的情绪分析，实现情绪条件化的时间序列预测。该模型将数值序列上的对抗学习与从非结构化文本中提取的情境化情绪表示相结合，联合建模以捕捉时间动态与外生信息。实验表明，混合生成与语言感知方法在非平稳环境中具有提升预测鲁棒性的潜力。
原文摘要 · Abstract (English)
The problem of time-series forecasting in non-stationary and complex environments is a challenging task in machine learning, especially with heterogeneous numerical and textual data present. Traditional statistical models like AutoRegressive Integrated Moving Average (ARIMA) are based on the assumptions of linearity and stationarity, whereas recurrent neural networks like Long Short-Term Memory (LSTM) models do not necessarily represent distributional properties in highly volatile settings. This paper proposes a hybrid model that combines Generative Adversarial Networks (GANs) with Natural Language Processing (NLP)-based sentiment analysis to enable sentiment-conditioned time-series prediction. The model integrates adversarial learning on numerical sequences with contextual sentiment representations derived from unstructured text, enabling them to be jointly modelled to capture temporal dynamics and exogenous information. These results demonstrate the promise of hybrid generative and language-aware methods to enhance prediction robustness in non-stationary environments.
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