forecasting

Google introduces TimesFM-3, a state-of-the-art zero-shot foundation model for multivariate time series forecasting, capable of handling multiple targets and covariates in a single forward pass without fine-tuning.

A comparative benchmarking study evaluates various AI methods for renewable energy farm optimization and forecasting, showing that ensemble and hybrid approaches excel in different data scenarios.

A survey paper that categorizes LLM-based agents for time-series tasks into four categories and provides a task-oriented guide for design and future research.

The article forecasts the AI industry's trajectory using expert predictions, highlighting continued revenue growth for companies like Anthropic and OpenAI, and significant increases in data center investment despite regulatory challenges.

NVExplain introduces a model-agnostic framework for explaining time series forecasting by analyzing latent trajectories and semantic flow, using structure-preserving surrogates to generate human-readable explanations with competitive faithfulness and stability.

This paper proposes a causal analysis framework to identify biases in time series foundation models, applied to Chronos-2 and TimesFM-2.5, revealing specific failure modes like overestimation of persistence and failures against regime switch patterns.

This paper investigates whether large language models trained on synthetic limit order book data develop an accurate world model, finding that while they generate valid sequences, they have systematic errors leading to biased and spurious forecasts.

This paper introduces QHAdamW, a modified optimizer for artificial neural networks, applied to air quality forecasting in the Philippines, showing improved convergence and performance in predicting PM2.5 and PM10 levels.

This paper evaluates machine learning and ARIMA models for adaptive public health forecasting using Ontario COVID-19 data, proposing an ensemble method called MLAMA for improved performance across different conditions.

This paper introduces SATS, a novel pretraining method for time series foundation models that uses scale-aware token alignment and hybrid masking to achieve state-of-the-art forecasting performance with enhanced efficiency across heterogeneous datasets.

The paper empirically characterizes the learning geometry of hybrid quantum forecasting models, comparing them to classical baselines using Neural Tangent Kernel dynamics and other metrics, showing that similar generalization can emerge from different optimization trajectories.

CLS introduces a scalable framework for simultaneous causal network inference and forecasting in dynamical systems, achieving high-fidelity reconstruction and accurate predictions in benchmarks.

This paper introduces DNBNet, a debiased neural basis-function network for irregular time series forecasting, addressing limitations in existing methods by correcting asymptotic bias and using adaptive neural basis functions.

This paper challenges the assumption that iterative denoising always improves forecasts in diffusion-based time series forecasting, proposing a global stopping criterion and a Bernoulli sampler to enhance accuracy and speed.

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.