论文标题
修剪帆：股票预测的二阶学习范例
Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction
论文作者
论文摘要
如今，机器学习方法已被广泛用于库存预测。传统方法假设一个相同的数据分布，根据该数据分布，在该数据分布下，培训数据上的学习模型是固定的，并直接应用于测试数据中。尽管这种假设使传统的机器学习技术在许多现实世界中成功成功，但股票市场的高度动态性质使股票预测的严格假设无效。为了应对这一挑战，我们提出了二阶相同的分布假设，其中假定数据分布随着时间的流逝而通过某些模式波动。基于这样的假设，我们开发了具有多尺度模式的二阶学习范式。关于现实世界中国股票数据的广泛实验证明了我们二阶学习范式在库存预测中的有效性。
Nowadays, machine learning methods have been widely used in stock prediction. Traditional approaches assume an identical data distribution, under which a learned model on the training data is fixed and applied directly in the test data. Although such assumption has made traditional machine learning techniques succeed in many real-world tasks, the highly dynamic nature of the stock market invalidates the strict assumption in stock prediction. To address this challenge, we propose the second-order identical distribution assumption, where the data distribution is assumed to be fluctuating over time with certain patterns. Based on such assumption, we develop a second-order learning paradigm with multi-scale patterns. Extensive experiments on real-world Chinese stock data demonstrate the effectiveness of our second-order learning paradigm in stock prediction.
