forecasting

WindBorne Systems, which uses AI-powered weather forecasting with long-flying balloons, raised a $37M Series B round co-led by Khosla Ventures and Galvanize to expand commercial applications and grow its customer base beyond government agencies.

This paper presents a schedule-informed Temporal Fusion Transformer framework for forecasting hourly airport security-checkpoint throughput, using flight schedules converted into temporally aligned screening-load signals. The model achieves lower prediction errors compared to RNN and LSTM baselines on Atlanta airport data.

This paper introduces a verifier-guided workflow around ODEFormer, a pretrained symbolic transformer, to discover interpretable equations for physical dynamical systems. It demonstrates transfer to vortex shedding and other systems using dynamical and physical-admissibility criteria to select equations from a candidate pool.

This paper introduces TraceMAS, an interactive demo system for traceable multi-agent forecasting that organizes agent outputs around causal-loop diagrams to make the evidence-to-forecast process inspectable, demonstrated on crude oil price forecasting.

A deep learning CNN model from Seoul National University forecast a very strong El Niño months ahead of NOAA's physics-based models, and has been validated as the models converged. The AI also predicts a La Niña flip in 2028, far beyond traditional forecast horizons.

The author trained an encoder-only transformer to predict future blood glucose levels from past glucose, carbs, insulin, and future meal/insulin inputs, releasing the MIT-licensed source code with pretrained weights.

This paper introduces OptimismBench, a benchmark that uses inverted pairs to detect directional bias in language model probability judgments. It finds that most models exhibit optimism bias, and that alignment (post-training) amplifies this tilt, with model identity dominating language effects.

This paper introduces Complementary Matrix Gating (CMG) for QKAN-based fast-weight programmers, enabling coordinate-wise memory control for quantum dynamics forecasting. The method shows consistent improvements and low mean-squared errors on quantum simulation benchmarks.

Describes a method for unifying outputs from thousands of agents in parallel forecasting tasks, achieving consistency and cost efficiency through post-processing and homogeneous task design.

This paper presents a hybrid probabilistic forecasting system that integrates time series decomposition (Prophet) with NLP techniques applied to Bolivian news coverage to predict roadblocks, achieving improved AUC-ROC and Brier Score over purely statistical models.

CARNet integrates global recurrent cycle information into efficient core-based interaction modeling for multivariate time series forecasting, achieving linear complexity and outperforming strong transformer baselines on real-world benchmarks.

Black-Mamba introduces a test-time adaptive forecasting architecture that uses accumulated surprisal to selectively update memory only upon evidence of distribution drift, achieving efficient adaptation on non-stationary time series.

This paper introduces LeadTime-ICL (LT-ICL), a censoring-aware in-context learning model for probabilistic supplier lead time forecasting. It combines a transformer backbone with a normalizing flow head and demonstrates strong performance across 24 industrial supply chain datasets without task-specific retraining.

Proposes a domain-agnostic framework for generating grounded natural language explanations for time series forecasts using large language models, reducing hallucination by constraining to verifiable evidence. Evaluated on financial and freight pricing case studies.

The article explores why time series forecasting is uniquely challenging compared to other machine learning tasks, presenting benchmark results showing that simple statistical models and zero-shot foundation models often outperform sophisticated deep learning models on many series.

This paper introduces a training-time stability regularization penalty to improve forecast stability without sacrificing accuracy, evaluated on M5 retail demand data, showing improvements in Forecast Stability Score while maintaining RMSE within 0.72%.

This paper presents ReDiTT, a retrieval augmented conditional diffusion transformer for asynchronous time series prediction. The model retrieves structurally similar latent sequences as reference conditions to improve long-horizon forecasting and sample diversity, achieving state-of-the-art performance on seven real-world datasets.

OmniPM-Net is a neural process fusion model that combines discrete station forecasts from graph neural networks with gridded forecasts from chemical transport models to produce consistent PM10 predictions at both stations and grid cells, improving accuracy especially during dust storms.

MIT researcher Devavrat Shah's work on AI models for tabular time-series data led to the spinoff Ikigai Labs, recently acquired by Celonis, aiming to improve enterprise forecasting and decision-making.

This article provides a visual walkthrough of t0-alpha, a 101.6M-parameter foundation model for multivariate time-series forecasting that separates time attention from cross-variable group attention, achieving competitive CRPS scores on GIFT-Eval compared to larger models like TimesFM 2.5 and Chronos-2.