# One Model to Forecast Them All and in Entity Distributions Bind Them

arXiv: https://arxiv.org/abs/2501.15499 · HTML: https://mlsanity.com/paper/2501.15499/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Improving Conditional Coverage via Orthogonal Quantile Regression](https://mlsanity.com/paper/2106.00394/) (2021)
- [A deep generative model for probabilistic energy forecasting in power systems: normalizing flows](https://mlsanity.com/paper/2106.09370/) (2021)
- [Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations](https://mlsanity.com/paper/2106.00006/) (2021)
- [Generating Contextual Load Profiles Using a Conditional Variational Autoencoder](https://mlsanity.com/paper/2209.04056/) (2022)
- [GUIDE-VAE: Advancing Data Generation with User Information and Pattern Dictionaries](https://mlsanity.com/paper/2411.03936/) (2024)
- [Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation](https://mlsanity.com/paper/2204.10770/) (2022)
- [Generative Probabilistic Time Series Forecasting and Applications in Grid Operations](https://mlsanity.com/paper/2402.13870/) (2024)

## Built on by (most cited first, 1 of 1)

- [The Living Forecast: Evolving Day-Ahead Predictions into Intraday Reality](https://mlsanity.com/paper/2510.12271/) (2025)

## Abstract (arXiv)

Probabilistic forecasting in power systems often involves multi-entity datasets like households, feeders, and wind turbines, where generating reliable entity-specific forecasts presents significant challenges. Traditional approaches require training individual models for each entity, making them inefficient and hard to scale. This study addresses this problem using GUIDE-VAE, a conditional variational autoencoder that allows entity-specific probabilistic forecasting using a single model. GUIDE-VAE provides flexible outputs, ranging from interpretable point estimates to full probability distributions, thanks to its advanced covariance composition structure. These distributions capture uncertainty and temporal dependencies, offering richer insights than traditional methods. To evaluate our GUIDE-VAE-based forecaster, we use household electricity consumption data as a case study due to its multi-entity and highly stochastic nature. Experimental results demonstrate that GUIDE-VAE outperforms conventional quantile regression techniques across key metrics while ensuring scalability and versatility. These features make GUIDE-VAE a powerful and generalizable tool for probabilistic forecasting tasks, with potential applications beyond household electricity consumption.