自适应信息路由用于多模态时间序列预测
Adaptive Information Routing for Multimodal Time Series Forecasting
AI总结：
本文提出自适应信息路由框架，通过动态利用文本信息优化时间序列模型，提升多模态预测的准确性。
摘要：
时间序列预测是人工智能中的关键任务，具有众多实际应用。传统方法主要依赖于历史时间序列数据来预测未来值。然而，在实际场景中，由于可用信息有限，这种方法往往难以实现准确预测。为了解决这一挑战，已经探索了多模态时间序列预测方法，这些方法主要结合了额外的数据模态，主要是文本数据，与时间序列数据一起使用。在本工作中，我们引入了自适应信息路由（AIR）框架，这是一种新的多模态时间序列预测方法。与现有方法不同，这些方法将文本数据与时间序列数据视为可互换的辅助特征进行预测，而AIR利用文本信息动态指导时间序列模型，通过控制如何以及在多大程度上结合多变量时间序列信息来实现这一点。我们还提出了一条文本精炼流程，利用大型语言模型将原始文本数据转换为适合多模态预测的形式，并介绍了一个基准测试，该测试基于此流程促进多模态预测实验。使用现实世界的数据，如原油价格和汇率，实验结果表明，AIR能够有效地利用文本输入调节时间序列模型的行为，在各种时间序列预测任务中显著提高了预测准确性。
英文摘要：
Time series forecasting is a critical task for artificial intelligence with numerous real-world applications. Traditional approaches primarily rely on historical time series data to predict the future values. However, in practical scenarios, this is often insufficient for accurate predictions due to the limited information available. To address this challenge, multimodal time series forecasting methods which incorporate additional data modalities, mainly text data, alongside time series data have been explored. In this work, we introduce the Adaptive Information Routing (AIR) framework, a novel approach for multimodal time series forecasting. Unlike existing methods that treat text data on par with time series data as interchangeable auxiliary features for forecasting, AIR leverages text information to dynamically guide the time series model by controlling how and to what extent multivariate time series information should be combined. We also present a text-refinement pipeline that employs a large language model to convert raw text data into a form suitable for multimodal forecasting, and we introduce a benchmark that facilitates multimodal forecasting experiments based on this pipeline. Experiment results with the real world market data such as crude oil price and exchange rates demonstrate that AIR effectively modulates the behavior of the time series model using textual inputs, significantly enhancing forecasting accuracy in various time series forecasting tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
