forecasting

A retail planner describes using AI agents built with Claude and ChatGPT to automate tasks like forecasting and reporting, improving efficiency in their daily work.

This paper introduces Predictive Memory Localization (PML), a method that forecasts selective intervention outcomes from internal model signals, distinguishing calibrated target movement from semantic-neighbor and capability damage. Experiments across 3,000 records and nine datasets show improved selective-path prediction and risk-aware intervention decisions.

A controlled ablation study of LLM self-reflection in conflict forecasting finds that typed action routing drives performance gains, while diagnostic scaffolding and taxonomy vocabulary add no measurable value, with replication on GPT-4o.

This paper investigates whether side effects of activation steering in language models can be predicted before intervention, constructing a cross-effect matrix across 67 behaviors and finding that side effects are systematic and forecastable from unsteered representations.

This paper investigates whether time-series forecasters use the correct historical inputs, separating recoverability of delays, model reporting, and functional use. It proves that accurate forecasts and correct delay reports can still hide the use of wrong lags, and demonstrates this issue empirically in N-HiTS and TCN models.

A research paper presents a latent neural differential equation framework that infers unknown blood-clotting parameters from sparse measurements and forecasts thrombus growth, with stochastic neural ODEs achieving the best predictive performance.

This paper identifies 'suboptimal collapse' in RL post-training of time series foundation models and proposes Ground-Truth Neighborhood Regularization (GTN-R) to keep output distributions near the ground truth, improving forecasting performance.

An analysis of six new 'neolab' AI startups with multi-billion-dollar funding, arguing that investors are betting against recursive self-improvement and superintelligence, while forecasting their compute, model release dates, and valuations.

Align-RAG introduces a training-free, closed-form alignment method for retrieval-augmented forecasting with frozen Time Series Foundation Models, outperforming learned fusion adapters on standard benchmarks without any learned parameters.

The paper introduces Crafter, an agent for corrective feature discovery that mines the residual of frozen black-box forecasters using compositional search and LLM-generated features, achieving up to 27% error reduction across six datasets and backbones.

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.