arXiv AI
Distributed JEPA: A Self-Supervised Framework for Energy Forecasting
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Aug 13
FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting
arXiv:2608. 11254v1 Announce Type: new Abstract: Accurate solar irradiance forecasting is essential for the reliable integration of photovoltaic power into modern electricity grids.
arXiv Machine Learning
Jun 8
CF-JEPA: Mask-free forward prediction with asymmetric encoder utilization for time-series representation learning
arXiv:2606. 07031v1 Announce Type: new Abstract: Self-supervised learning (SSL) for time-series representation learning is dominated by two paradigms: contrastive methods, which face challenges in constructing positive or negative pairs, and masking-based methods, which disrupt the temporal continuity of time-series signals.
arXiv Machine Learning
Jun 30
Improving Coherence in Hierarchical Time Series Forecasting using Structured Temporal Fusion
arXiv:2606. 28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures.
arXiv Machine Learning
5d ago
WorldTS: World Modeling for Multimodal Covariate-aware Time Series Forecasting
WorldTS is a new forecasting framework that models latent dynamics conditioned on multimodal covariates to improve time‑series prediction. It uses a two‑stage training process: first learning latent state dynamics from historical data and covariates, then training a decoder to map predicted latent states back to future observations. Experiments on 21 real‑world datasets demonstrate the effectiveness of this approach.
arXiv AI
Jul 21
FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting
arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.
arXiv Machine Learning
Aug 21
CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
arXiv:2608. 20025v1 Announce Type: new Abstract: Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation.