# Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification

arXiv: https://arxiv.org/abs/2209.04526 · HTML: https://mlsanity.com/paper/2209.04526/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Accurate Uncertainties for Deep Learning Using Calibrated Regression](https://mlsanity.com/paper/1807.00263/) (2018)
- [Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks](https://mlsanity.com/paper/1605.07127/) (2016)
- [Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories](https://mlsanity.com/paper/1806.05357/) (2018)
- [Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians](https://mlsanity.com/paper/1910.12288/) (2019)
- [Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks](https://mlsanity.com/paper/2106.00884/) (2021)

## Built on by (most cited first, 17 of 17)

- [GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks](https://mlsanity.com/paper/2410.05780/) (2024)
- [AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on AI-READI Dataset](https://mlsanity.com/paper/2502.09919/) (2025)
- [Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting](https://mlsanity.com/paper/2411.10703/) (2024)
- [Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes](https://mlsanity.com/paper/2502.14183/) (2025)
- [Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models](https://mlsanity.com/paper/2309.16521/) (2023)
- [Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting](https://mlsanity.com/paper/2407.19784/) (2024)
- [Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach](https://mlsanity.com/paper/2406.15346/) (2024)
- [Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes](https://mlsanity.com/paper/2506.07864/) (2025)
- [The Driver-Blindness Phenomenon: Why Deep Sequence Models Default to Autocorrelation in Blood Glucose Forecasting](https://mlsanity.com/paper/2511.20601/) (2025)
- [From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent](https://mlsanity.com/paper/2608.13581/) (2026)
- [MoCA: Multi-modal Cross-masked Autoencoder for Time Series in Digital Health](https://mlsanity.com/paper/2506.02260/) (2025)
- [GluMind: Multimodal Parallel Attention and Knowledge Retention for Robust Cross-Population Blood Glucose Forecasting](https://mlsanity.com/paper/2509.18457/) (2025)
- [From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting](https://mlsanity.com/paper/2605.00645/) (2026)
- [MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes](https://mlsanity.com/paper/2606.18640/) (2026)
- [Subject-Conditioned Glucose Forecasting in Type-1 Diabetes](https://mlsanity.com/paper/2607.19006/) (2026)
- [FairGlucose: A CGM Fairness Benchmark Reveals Subgroup Disparities Hidden in Population-Level Validation](https://mlsanity.com/paper/2608.18296/) (2026)
- [Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting](https://mlsanity.com/paper/2609.11872/) (2026)

## Abstract (arXiv)

Deep learning models achieve state-of-the art results in predicting blood glucose trajectories, with a wide range of architectures being proposed. However, the adaptation of such models in clinical practice is slow, largely due to the lack of uncertainty quantification of provided predictions. In this work, we propose to model the future glucose trajectory conditioned on the past as an infinite mixture of basis distributions (i.e., Gaussian, Laplace, etc.). This change allows us to learn the uncertainty and predict more accurately in the cases when the trajectory has a heterogeneous or multi-modal distribution. To estimate the parameters of the predictive distribution, we utilize the Transformer architecture. We empirically demonstrate the superiority of our method over existing state-of-the-art techniques both in terms of accuracy and uncertainty on the synthetic and benchmark glucose data sets.