forecasting

SurF is a generative model for multivariate irregularly sampled time series using the Time Rescaling Theorem to transform event sequences into i.i.d. exponential noise, achieving state-of-the-art results across multiple real-world benchmarks.

Datadog releases Toto 2.0, a family of open-weights time series foundation models from 4M to 2.5B parameters, achieving state-of-the-art results on three benchmarks. The models demonstrate scaling laws for time series, improving predictably with parameter count.

This paper introduces engagement forecasting for intelligent tutoring systems, predicting weekly minutes practiced and new skills mastered using interaction logs from 425 middle-school students. Feature-based models reduce error by 22-33% over heuristic baselines, offering explainable patterns for tutor-learner goal setting.

FutureSim replays chronological world events to benchmark AI agents' long-term predictive abilities, finding that even the best agent achieves only 25% accuracy.

The article highlights a performance rank-order flip between Claude Opus and Gemini Pro on a forecasting benchmark, depending on whether models perform their own web research or are given fixed evidence. This suggests that Opus excels at the research phase while Gemini is superior at judgment over fixed evidence, exposing a mismatch between standard benchmarks and actual deployment conditions.

A research paper proposing a four-stage hybrid framework for solar and wind energy forecasting, utilizing a quantum-inspired variational kernel for residual correction and a generative AI layer for explainability.

An analysis of prediction markets like Polymarket and Kalshi, examining whether their massive trading volume actually produces valuable forecasting information or merely serves as gambling, referencing historical academic support and current data.

Prediction markets are increasingly influencing news coverage and becoming a subject of journalism in their own right, as platforms like Polymarket gain mainstream attention for forecasting real-world events.

Google open-sourced TimesFM 2.5, a 200 M-parameter, 16 k-context zero-shot time-series forecasting base model that works straight out of the box on historical data.

Google DeepMind releases WeatherNext 2, an advanced AI model that generates faster, higher-resolution global weather forecasts and hundreds of scenarios in under a minute using a single TPU.

This article presents a research paper on Time-Series Foundation Model (TimeFM), a decoder-only model that achieves near-optimal zero-shot performance across diverse time-series datasets by adapting large language model techniques.