# Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories

arXiv: https://arxiv.org/abs/1806.05357 · HTML: https://mlsanity.com/paper/1806.05357/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 15 of 15)

- [Domain Adaptation for Time Series Forecasting via Attention Sharing](https://mlsanity.com/paper/2102.06828/) (2021)
- [Do We Really Need Deep Learning Models for Time Series Forecasting?](https://mlsanity.com/paper/2101.02118/) (2021)
- [Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification](https://mlsanity.com/paper/2209.04526/) (2022)
- [GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks](https://mlsanity.com/paper/2410.05780/) (2024)
- [Deep Time Series Forecasting with Shape and Temporal Criteria](https://mlsanity.com/paper/2104.04610/) (2021)
- [Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction](https://mlsanity.com/paper/2101.06850/) (2021)
- [Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks](https://mlsanity.com/paper/2106.00884/) (2021)
- [Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose](https://mlsanity.com/paper/2304.08593/) (2023)
- [EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables](https://mlsanity.com/paper/2208.01913/) (2022)
- [Global Deep Forecasting with Patient-Specific Pharmacokinetics](https://mlsanity.com/paper/2309.13135/) (2023)
- [Likelihood-based generalization of Markov parameter estimation and multiple shooting objectives in system identification](https://mlsanity.com/paper/2212.13902/) (2022)
- [Recursive input and state estimation: A general framework for learning from time series with missing data](https://mlsanity.com/paper/2104.08556/) (2021)
- [The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Management](https://mlsanity.com/paper/2310.14743/) (2023)
- [From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting](https://mlsanity.com/paper/2605.00645/) (2026)
- [A Proof-of-Concept Simulation-Driven Digital Twin Framework for Decision-Aware Diabetes Modeling](https://mlsanity.com/paper/2605.11247/) (2026)

## Abstract (arXiv)

In many forecasting applications, it is valuable to predict not only the value of a signal at a certain time point in the future, but also the values leading up to that point. This is especially true in clinical applications, where the future state of the patient can be less important than the patient's overall trajectory. This requires multi-step forecasting, a forecasting variant where one aims to predict multiple values in the future simultaneously. Standard methods to accomplish this can propagate error from prediction to prediction, reducing quality over the long term. In light of these challenges, we propose multi-output deep architectures for multi-step forecasting in which we explicitly model the distribution of future values of the signal over a prediction horizon. We apply these techniques to the challenging and clinically relevant task of blood glucose forecasting. Through a series of experiments on a real-world dataset consisting of 550K blood glucose measurements, we demonstrate the effectiveness of our proposed approaches in capturing the underlying signal dynamics. Compared to existing shallow and deep methods, we find that our proposed approaches improve performance individually and capture complementary information, leading to a large improvement over the baseline when combined (4.87 vs. 5.31 absolute percentage error (APE)). Overall, the results suggest the efficacy of our proposed approach in predicting blood glucose level and multi-step forecasting more generally.