forecasting

An open source AI trading stack: Kronos serves as the financial candlestick foundation model for market prediction, while day-trader-skill acts as a Claude-powered day trading toolkit covering market scanning, news monitoring, position risk management, and order execution. Together they form a complete trading workflow.

The paper proposes QUAL, a label-free framework that aligns quantiles of time-series foundation models using the model pool as a probability reference, then applies Huber aggregation for robust zero-shot forecasting, reducing average member MASE by 7.4% and CRPS by 9.8% on GIFT-Eval.

论文提出 Investor's Last Exam (ILE)，一个由买方投资者主导的股票研究基准，评估AI智能体在冻结信息集下的研究质量、预测准确度与模拟投资表现，结果显示最强模型（GPT-6 Astra、Claude Opus 5.5）研究能力强但预测表现仍显著不足。

A Wired reporter deployed a swarm of AI agents to manage a fantasy Premier League squad, finding they still underperform humans, while new research shows frontier models like GPT-5 increasingly excel at real-world prediction tasks and forecasting benchmarks such as Prophet Arena.

Investor's Last Exam (ILE) 是一个由资深买方投资者主导的股票研究基准，使用冻结证据、专家评分标准和金融依赖图评估 AI 智能体的端到端研究能力，结果显示顶尖模型研究能力强但在预测和投资判断上仍然失败。

该论文提出了多变量时间序列超分辨率预测（MTS-SRF）任务及 TimeMagnifier 模型，能够从粗粒度历史数据中预测细粒度未来，在十个数据集上以 12.3% 的平均 MSE 降低击败 15 个基线。

A preregistered causal audit finds that covariate-aware time series foundation models (Chronos-2, TiRex-2, TabPFN-TS, TimesFM-2.5) forecast associations rather than intervention effects, with a derived confounding gap that can be reduced up to 83% via brief interventional post-training with LoRA adapters.

Timestral is a family of four time series foundation models (5M to 209M parameters) achieving state-of-the-art probabilistic forecasting on GIFT-Eval, TIME, and fev-bench via decoupled horizon forecasting, native future covariate support, and synthetic pretraining followed by mixed post-training.

Opinion piece arguing that rather than being overhyped, AI progress over the past 18 months has been consistently underestimated by most observers and experts.

IBM Research 发布 Granite Timeseries Ensemble R1，将四个互补的开源 Granite 时序预测模型组合成一个统一接口，提供概率与分位数预测，无需重训练即可作为默认选择使用。

The paper identifies that existing time series tokenization strategies (patch and phase tokenization) introduce spectral distortions such as spectral leakage and aliasing, and proposes TimeFT, a parameter-free, nearly distortion-free frequency-based tokenizer that serves as a drop-in replacement and improves performance across forecasting, classification, and anomaly detection tasks.

Demis Hassabis has updated his AGI timeline estimate to 2-3 years, down from his earlier 3-5 year prediction, signaling growing confidence in rapid AI progress.

本文提出一个可观察证据标准，用于评估工业AI规划系统中新增能力是否真正带来运营价值，并通过Favorita、M5和Walmart数据的回放实验揭示单一指标评估的三大局限。

Meta-iLaD 提出一种通过跨动力学环境元学习实现可识别潜在动力学的新框架，利用少量样本的条件先验快速提取环境变量，并同时对潜在状态、环境变量与动力学函数建立可识别性，从而提升对未见环境的泛化与预测能力。

Cloudflare announces a preview of Clairic, the first System One-class extraction model, hosted on Workers AI as @cf/cloudflare/clairic with open-weights planned for release. Clairic extracts, computes, estimates, and forecasts typed values from unstructured inputs with higher accuracy, lower latency, and lower cost than traditional LLMs.

Mistral 发布 Timestral 时间序列基础模型家族，包含 5M 到 209M 参数四个版本，其 209M 模型在 GIFT-Eval、TIME 和 fev-bench 三大基准上均排名第一，支持零样本概率多变量预测且可在 CPU 上推理。

An AI 2027 co-author, ranked the #2 most accurate AI forecaster, warns that without slowing AI development there is a two-thirds probability of an AI takeover. The claim adds to growing expert concern over pace-of-progress risks and calls for deceleration.

该论文提出CGDT-CESN——一种基于变量相关性引导并进行跨簇权重训练的聚类回声状态网络，用于多变量时间序列预测，在Twitter信息级联与太阳风预测数据集上优于传统ESN。

Over the past seven months, AI models have consistently been solving open Erdős problems that mathematicians had failed to crack. In San Francisco, the industry's expected timeline for AGI has shrunk to 10 years, and the overall pace of change far exceeds what most people imagine.

Anima Anandkumar 介绍其发表在 PNAS 的最新论文，提出循环神经算子（RNO）用于在气候模拟中提前两年以上预测临界点发生的概率与时间（误差约 11 天），并在机翼气流模拟中验证了其在航空航天设计中的潜力。