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.

本文提出了一种新的基于语言基础模型的时间序列模式挖掘流程，以用于人类移动预测任务，该模型通过引入特定提示将数值时间序列转换为句子，以便直接应用于现有语言模型，设计了一个 AuxMobLCast 流程用于预测每个 POI 的访客数量，结合了辅助 POI 分类和编码器 - 解码器架构，实证证明了该流程在移动性预测任务中发现序列模式的有效性，并在三个真实数据集上进行了评估，表现良好。

利用语言基础模型进行人类移动预测

Leveraging Language Foundation Models for Human Mobility Forecasting

Existing human mobility forecasting models follow the standard design of the
time-series prediction model which takes a series of numerical values as input
to generate a numerical value as a prediction. Although treating this as a
regression problem seems straightforward, incorporating various contextual
information such as the semantic category information of each Place-of-Interest
(POI) is a necessary step, and often the bottleneck, in designing an effective
mobility prediction model. As opposed to the typical approach, we treat
forecasting as a translation problem and propose a novel forecasting through a
language generation pipeline. The paper aims to address the human mobility
forecasting problem as a language translation task in a sequence-to-sequence
manner. A mobility-to-language template is first introduced to describe the
numerical mobility data as natural language sentences. The core intuition of
the human mobility forecasting translation task is to convert the input
mobility description sentences into a future mobility description from which
the prediction target can be obtained. Under this pipeline, a two-branch
network, SHIFT (Translating Human Mobility Forecasting), is designed.
Specifically, it consists of one main branch for language generation and one
auxiliary branch to directly learn mobility patterns. During the training, we
develop a momentum mode for better connecting and training the two branches.
Extensive experiments on three real-world datasets demonstrate that the
proposed SHIFT is effective and presents a new revolutionary approach to
forecasting human mobility.

本文提出一种基于语言生成的人类移动趋势预测方法 SHIFT，通过将输入移动趋势描述句子转化为未来的移动状态描述，提高了移动预测模型的有效性和准确性。