Deep Video Prediction for Time Series Forecasting
利用深度视频预测进行时间序列预测AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确提出多变量金融价格时间序列预测方法，并进行预测基线比较。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
利用深度视频预测进行时间序列预测
- 研究问题
- 能否把多个潜在相互作用的金融资产价格转化为图像序列，利用视频预测方法改善未来价格路径的联合预测？
- 方法与思路
- 作者将各时点资产价格排列为二维图像，用卷积神经网络学习潜在表示，再借助视频预测方法生成未来价格图像。
- 材料陈述的贡献
- 摘要称在九项美国股票市场交易资产的价格预测实验中，该方法优于ARIMA、Prophet及所比较的方法变体。
- 阅读关注点
- 应核对资产二维排列依据、数据划分和信息泄漏防范，以及预测期限、评价指标与基线调参是否保证比较公平。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未给出误差数值与检验结果，价格预测表现不能直接推导收益预测能力。
原文摘要
Time series forecasting is essential for decision making in many domains. In this work, we address the challenge of predicting prices evolution among multiple potentially interacting financial assets. A solution to this problem has obvious importance for governments, banks, and investors. Statistical methods such as Auto Regressive Integrated Moving Average (ARIMA) are widely applied to these problems. In this paper, we propose to approach economic time series forecasting of multiple financial assets in a novel way via video prediction. Given past prices of multiple potentially interacting financial assets, we aim to predict the prices evolution in the future. Instead of treating the snapshot of prices at each time point as a vector, we spatially layout these prices in 2D as an image, such that we can harness the power of CNNs in learning a latent representation for these financial assets. Thus, the history of these prices becomes a sequence of images, and our goal becomes predicting future images. We build on a state-of-the-art video prediction method for forecasting future images. Our experiments involve the prediction task of the price evolution of nine financial assets traded in U.S. stock markets. The proposed method outperforms baselines including ARIMA, Prophet, and variations of the proposed method, demonstrating the benefits of harnessing the power of CNNs in the problem of economic time series forecasting.