# Unlocking Insights into Business Trajectories with Transformer-based Spatio-temporal Data Analysis

arXiv: https://arxiv.org/abs/2306.10034 · HTML: https://mlsanity.com/paper/2306.10034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Query2Label: A Simple Transformer Way to Multi-Label Classification](https://mlsanity.com/paper/2107.10834/) (2021)
- [More Identifiable yet Equally Performant Transformers for Text Classification](https://mlsanity.com/paper/2106.01269/) (2021)

## Abstract (arXiv)

The world of business is constantly evolving and staying ahead of the curve requires a deep understanding of market trends and performance. This article addresses this requirement by modeling business trajectories using news articles data.