IBM/TabFormer
Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)
This project helps data scientists and machine learning engineers analyze complex patterns in sequences of events. It takes in structured tabular data, like credit card transactions or air quality measurements over time, and applies advanced transformer models to identify relationships and predict future events. The output is a trained model that can be used for tasks like anomaly detection or forecasting.
360 stars. No commits in the last 6 months.
Use this if you are a data scientist working with event sequences and need to build sophisticated models to understand or predict behavior from detailed, multi-dimensional records.
Not ideal if you are looking for an off-the-shelf application to solve a business problem directly, as this provides foundational model building tools for developers.
Stars
360
Forks
90
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 17, 2025
Commits (30d)
0
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