基于文本的时间序列变量预测特征构建研究

Yiren Wang, Dominic Seyler, Shubhra (Santu) Karmaker, ChengXiang Zhai
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引用次数: 11

摘要

时间序列在世界上无处不在,因为它们被用来测量各种现象(例如,温度、病毒传播、销售等)。时间序列的预测对优化决策非常有益(也是必要的),但也是一个非常具有挑战性的问题;仅使用时间序列的历史值通常是不够的。本文研究了如何基于相关文本数据构建有效的附加特征用于时间序列预测。除了常用的n-gram特征外,我们还提出了一种基于主题模型发现的主题构建多个主题特征的通用策略。我们使用预测股票价格变化的数据集来评估特征的有效性,其中我们从新闻文本文章中构建了用于股票市场预测的附加特征。我们发现:1)基于文本的特征优于基于时间序列的特征,这表明利用文本数据来改进时间序列预测的巨大前景。2)基于主题的特征在单独使用时不是很有效,但是当添加到n-gram特征上时,它们可以进一步提高性能。3)最好的基于主题的特征似乎是一个长期的主题聚合,随着时间的推移,最近的主题权重很高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study of Feature Construction for Text-based Forecasting of Time Series Variables
Time series are ubiquitous in the world since they are used to measure various phenomena (e.g., temperature, spread of a virus, sales, etc.). Forecasting of time series is highly beneficial (and necessary) for optimizing decisions, yet is a very challenging problem; using only the historical values of the time series is often insufficient. In this paper, we study how to construct effective additional features based on related text data for time series forecasting. Besides the commonly used n-gram features, we propose a general strategy for constructing multiple topical features based on the topics discovered by a topic model. We evaluate feature effectiveness using a data set for predicting stock price changes where we constructed additional features from news text articles for stock market prediction. We found that: 1) Text-based features outperform time series-based features, suggesting the great promise of leveraging text data for improving time series forecasting. 2) Topic-based features are not very effective stand-alone, but they can further improve performance when added on top of n-gram features. 3) The best topic-based feature appears to be a long-term aggregation of topics over time with high weights on recent topics.
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