  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.
