Data-Driven Forecasting
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Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
Announce Type: cross Abstract: Forecasting seismic waveforms beyond observed data remains challenging due to the nonlinear, dispersive, and multi-scale nature of seismic wave propagation. In this work, we introduce \textsc{SeismoGPT}, a transformer-based autoregressive model designed to forecast three-component seismic waveforms directly in the time domain. Forecasting is formulated as a physically constrained continuation problem in which the model receives waveform context beginning at the...
Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
Announce Type: cross Abstract: Forecasting seismic waveforms beyond observed data remains challenging due to the nonlinear, dispersive, and multi-scale nature of seismic wave propagation. In this work, we introduce \textsc{SeismoGPT}, a transformer-based autoregressive model designed to forecast three-component seismic waveforms directly in the time domain. Forecasting is formulated as a physically constrained continuation problem in which the model receives waveform context beginning at the...
Uncovering Insights of Compound Flooding with Data-Driven AI
Announce Type: replace Abstract: Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention. Existing forecasting approaches, whether physics-based or data-driven, often emphasize temporal patterns while underexploring how multiple interacting factors jointly shape flood dynamics. To address this problem, we conduct a large-scale data-driven analysis of compound flooding in South Florida, a typical area...
A Data-Driven Methodology for Scalable Distributed MPC in Heterogeneous Building Aggregation: From Systematic Feature Selection to Convex Optimization
arXiv:2605.30763v1 Announce Type: new Abstract: Coordinating large-scale, heterogeneous building aggregations for demand response (DR) is impeded by a dual challenge: the computational intractability of centralized Model Predictive Control (MPC) and the inadequacy of conventional feature selection methods, which fail to address the error-compounding nature of multi-step forecasting required by MPC. This paper proposes a comprehensive, data-driven framework that first employs a systematic,...
Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management
new Abstract: The coffee supply chain is one of the most complex agri-food networks, marked by geographically dispersed production, multi-tier coordination, and high sensitivity to quality and freshness. While sustainability and digitalization have gained attention, demand forecasting, optimization, and traceability are often treated separately. This study presents a two-phase integrated framework.
Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
arXiv:2606.02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems. Volatile power generation, varying electricity tariffs, and increased entities, e.g., PV systems, and heat pumps, have increased the complexity and made the system harder to operate. This leads to the demand for additional...
Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
arXiv:2605.15470v2 Announce Type: replace Abstract: Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for ocean forecasting, applicable to both global and regional domains. Njord combines a deep latent variable framework with a graph neural network architecture, enabling sampling each forecast step in a single forward pass.
Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
arXiv:2605.15470v2 Announce Type: replace-cross Abstract: Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for ocean forecasting, applicable to both global and regional domains. Njord combines a deep latent variable framework with a graph neural network architecture, enabling sampling each forecast step in a single forward pass.
Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning
Announce Type: replace Abstract: Rapid global population growth underscores the need for digitally enabled agricultural systems that support sustainable food production and data-driven resource management for farmers and stakeholders. The adoption of Internet of Things (IoT) technologies, capable of capturing real-time environmental (e.g., temperature, humidity) and operational (e.g., irrigation) parameters, is a crucial step toward enabling advanced applications such as AI-based yield...
Probabilistic Precipitation Nowcasting with Rectified Flow Transformers
arXiv:2605.31204v1 Announce Type: new Abstract: Accurate weather forecasts are essential across various domains and are safety-critical in extreme weather conditions. Compared to simulation-based forecasting, data-driven approaches show greater efficiency, enabling short-term, high-resolution nowcasting. In particular, diffusion models proved effective in weather nowcasting due to their strong probabilistic foundation.