论文标题

利用语言基础模型以预测人类流动性

Leveraging Language Foundation Models for Human Mobility Forecasting

论文作者

Xue, Hao, Voutharoja, Bhanu Prakash, Salim, Flora D.

论文摘要

在本文中,我们提出了一条新型的管道,该管道利用语言基础模型进行时间顺序模式挖掘,例如人类流动性预测任务。例如,在预测利益(POI)客户流量的任务中,通常从历史日志中提取访问次数,并且仅使用数值数据来预测访客流。在这项研究中,我们直接对包含各种信息的自然语言输入执行预测任务,例如数值和上下文的语义信息。引入特定的提示以将数值时间序列转换为句子,以便可以直接应用现有的语言模型。我们设计了一个Auxmoblcast管道,用于预测每个POI中的访问者数量,并将辅助POI类别分类任务与编码器decoder架构集成在一起。这项研究提供了所提出的Auxmoblcast管道有效性以发现移动性预测任务中的顺序模式的经验证据。在三个现实世界数据集上评估的结果表明,预训练的语言基础模型在预测时间序列中也具有良好的性能。这项研究可以提供有远见的见解,并为预测人类流动性提供新的研究方向。

In this paper, we propose a novel pipeline that leverages language foundation models for temporal sequential pattern mining, such as for human mobility forecasting tasks. For example, in the task of predicting Place-of-Interest (POI) customer flows, typically the number of visits is extracted from historical logs, and only the numerical data are used to predict visitor flows. In this research, we perform the forecasting task directly on the natural language input that includes all kinds of information such as numerical values and contextual semantic information. Specific prompts are introduced to transform numerical temporal sequences into sentences so that existing language models can be directly applied. We design an AuxMobLCast pipeline for predicting the number of visitors in each POI, integrating an auxiliary POI category classification task with the encoder-decoder architecture. This research provides empirical evidence of the effectiveness of the proposed AuxMobLCast pipeline to discover sequential patterns in mobility forecasting tasks. The results, evaluated on three real-world datasets, demonstrate that pre-trained language foundation models also have good performance in forecasting temporal sequences. This study could provide visionary insights and lead to new research directions for predicting human mobility.

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