一个高效的实时股票预测,利用增量学习和深度学习
Tinku Singh1, Riya Kalra1, Suryanshi Mishra2
1Department of IT, Indian Institute of Information Technology Allahabad, Prayagraj, U.P. India.
Evolving systems
|April 16, 2024
概括
离线-在线学习模型提供比增量学习模型更准确的日内股票预测. 这些模型不断适应实时市场数据,以提高预测准确度.
科学领域:
- * 金融预测和算法交易.
- * 机器学习在定量金融中的应用.
背景情况:
- *日内股票交易依赖于短期价格波动,需要实时预测.
- * 股票市场的复杂性,波动性和非静止性对准确的预测构成重大挑战.
- * 传统的机器学习模型需要对当前数据进行超参数调整,以获得最佳性能.
研究的目的:
- * 提出和评估新的机器学习策略,用于实时的日内股票价格预测.
- * 为了比较增量学习与线下-线上学习的有效性,用于实时市场预测.
- * 评估单变量和多变量时间序列数据上的模型性能.
主要方法:
- * 实现增量学习:通过实时数据流不断更新模型.
- * 实现线下-线上学习:每次交易会后定期重新培训模型.
- *适用于单变量 (历史价格) 和多变量 (价格 + 技术指标) 时间序列数据.
- *对NASDAQ和NSE的八个流动股进行测试.
主要成果:
- * 与增量学习模型相比,离线-在线学习模型表现出更高的性能.
- *在采用离线-在线方法的模型中,预测误差明显较低.
- *这两种方法都应用于单变量和多变量时间序列数据,结果各不相同.
结论:
- *离线-在线学习是一种更有效的策略,用于在实时市场准确的日内股票价格预测.
- * 定期的再培训比持续的增量更新更好地捕捉到市场的复杂性.
- *这些发现为开发自适应算法交易策略提供了有价值的见解.
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