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于慧慧,戴群.基于自适应增量集成学习的非平稳金融时间序列预测[J].数据采集与处理,2021,36(5):1030-1040
基于自适应增量集成学习的非平稳金融时间序列预测
Non-stationary Financial Time Series Prediction Based on Self-adaptive Incremental Ensemble Learning
投稿时间:2020-09-24  修订日期:2020-10-24
DOI:10.16337/j.1004-9037.2021.05.018
中文关键词:  非平稳金融时间序列预测  自适应增量集成学习  数据权重  基模型权重
英文关键词:non-stationary financial time series prediction (NS-FTSP)  self-adaptive incremental ensemble learning (SIEL)  data weight  base model weight
基金项目:国家自然科学基金(61473150)资助项目。
作者单位邮编
于慧慧 南京航空航天大学计算机科学与技术学院/人工智能学院南京 211106 211106
戴群 南京航空航天大学计算机科学与技术学院/人工智能学院南京 211106 211106
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中文摘要:
      金融市场对于社会经济的发展非常重要,因此金融时间序列预测(Financial time series prediction, FTSP)一直是人们研究的焦点。至今,许多基于统计分析和软计算的方法被提出以解决FTSP问题,其中大多数方法将金融时间序列(Financial time series, FTS)视为或转化为平稳序列进行处理。但是,由于绝大部分FTS是非平稳的,因此这些方法通常存在伪回归或预测性能不佳等问题。本文提出了一种自适应增量集成学习(Self-adaptive incremental ensemble learning, SIEL)算法,用于解决非平稳金融时间序列预测(Non-stationary FTSP, NS-FTSP)问题。SIEL算法的主要思想是为每个非平稳金融时间序列(Non-stationary FTS, NS-FTS)子集增量地训练一个基模型,然后使用自适应加权规则将各基模型组合起来。SIEL算法的重点在于数据权重和基模型权重的更新:数据权重基于当前集成模型在最新数据集上的性能进行更新,其目的不是为了数据采样,而是为了权衡误差;基模型权重基于其所处环境进行自适应更新,且基模型在越新环境下的性能应具有越高的权重。此外,针对NS-FTS的特征,SIEL算法提出了一种能协调新旧知识以及应对环境重演的策略。最后,给出了SIEL算法在3个NS-FTS数据集上的实验结果,并将其与已有算法进行了对比。实验结果表明,SIEL算法能很好地解决NS-FTSP问题。
英文摘要:
      The financial market is very important to the development of social economy, so financial time series prediction (FTSP) has always been the research focus. So far, many methods based on statistical analysis and soft computing have been proposed to solve FTSP problems, most of which treat financial time series (FTS) as or convert them into stationary time series. However, since most FTSs are non-stationary, these methods usually have problems such as false regression or poor prediction performance. Therefore, this paper proposes a novel self-adaptive incremental ensemble learning (SIEL) method to solve the problem of non-stationary FTSP (NS-FTSP). The main idea of ??the SIEL algorithm is to incrementally train a base model for each non-stationary financial time series (NS-FTS) subset, and then ensemble the base models using the adaptive weighting rule. The focus of the SIEL algorithm is the update of data weight and base model weight. The weight of data is updated based on the performance of the current ensemble model on the latest dataset, and its purpose is not to sample the data, but to weigh the error; the weight of the base model is adaptively updated based on its environment, and the performance of the base model in the newer environment should have a higher weight. In addition, in view of the characteristics of NS-FTS, the SIEL algorithm proposes a strategy to coordinate new and old knowledge and cope with the recurrence of the environment. Finally, the paper gives the experimental results of the SIEL algorithm on three NS-FTS datasets and compares them with the existing algorithms. Experimental results show that the SIEL algorithm can solve the NS-FTSP problem well.
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