forecasting
30 articles about forecasting in AI news
New Research: Fine-Tuned LLMs Outperform GPT-5 for Probabilistic Supply Chain Forecasting
Researchers introduced an end-to-end framework that fine-tunes large language models (LLMs) to produce calibrated probabilistic forecasts of supply chain disruptions. The model, trained on realized outcomes, significantly outperforms strong baselines like GPT-5 on accuracy, calibration, and precision. This suggests a pathway for creating domain-specific forecasting models that generate actionable, decision-ready signals.
Google Open-Sources TimesFM: A 100B-Point Time Series Foundation Model for Zero-Shot Forecasting
Google has open-sourced TimesFM, a foundation model for time series forecasting trained on 100 billion real-world time points. It requires no dataset-specific training and can generate predictions instantly for domains like traffic, weather, and demand.
How AI is Impacting Five Demand Forecasting Roles in Retail
AI is transforming demand forecasting, shifting roles from manual data processing to strategic analysis. The article identifies five key positions being reshaped, highlighting a move towards higher-value, AI-augmented work.
Smarter Shopping: Forecasting the Future of AI Agents in Retail
The Wall Street Journal reports on the emerging role of autonomous AI agents in retail, forecasting their potential to transform shopping by handling complex, multi-step tasks. This signals a shift from passive chatbots to active, goal-oriented assistants.
New Research Identifies Data Quality as Key Bottleneck in Multimodal Forecasting
A new arXiv paper introduces CAF-7M, a 7-million-sample dataset for context-aided forecasting. The research shows that poor context quality, not model architecture, has limited multimodal forecasting performance. This has implications for retail demand prediction that combines numerical data with text or image context.
TimeSqueeze: A New Method for Dynamic Patching in Time Series Forecasting
Researchers introduce TimeSqueeze, a dynamic patching mechanism for Transformer-based time series models. It adaptively segments sequences based on signal complexity, achieving up to 20x faster convergence and 8x higher data efficiency. This addresses a core trade-off between accuracy and computational cost in long-horizon forecasting.
Beyond Simple Predictions: How Frequency Domain AI Transforms Retail Demand Forecasting
New FreST Loss AI technique analyzes retail data in joint spatio-temporal frequency domain, capturing complex dependencies between stores, products, and time for superior demand forecasting accuracy.
TimeGS: How Computer Graphics Techniques Are Revolutionizing Time Series Forecasting
Researchers have introduced TimeGS, a novel AI framework that treats time series forecasting as a 2D rendering problem. By adapting Gaussian splatting techniques from computer graphics, the approach achieves state-of-the-art performance while maintaining temporal continuity.
StaTS AI Model Revolutionizes Time Series Forecasting with Adaptive Noise Schedules
Researchers introduce StaTS, a diffusion model that learns adaptive noise schedules and uses frequency guidance for superior time series forecasting. The approach addresses key limitations in existing methods while maintaining efficiency.
Google's TimesFM Foundation Model: A New Paradigm for Time Series Forecasting
Google Research has open-sourced TimesFM, a 200 million parameter foundation model for time series forecasting. Trained on 100 billion real-world time points, it demonstrates remarkable zero-shot forecasting capabilities across diverse domains without task-specific training.
AI-Powered Geopolitical Forecasting: How Machine Learning Models Are Predicting Regime Stability
Advanced AI systems are now analyzing political instability with unprecedented accuracy, predicting regime vulnerabilities in real-time. These models process vast datasets to forecast governmental collapse and potential conflict escalation.
AI-2027 Authors Accelerate AGI Timelines, Citing Rapid Progress in Agentic Coding
The AI-2027 forecasting group has accelerated its timeline for when AI could replace human software engineers by 1.5 years, from late 2029 to mid-2028. This revision is based on observed rapid progress in agentic coding systems over the last 3-5 months.
Beyond Blue Books: How Real-Time Market Intelligence AI is Transforming Luxury Asset Valuation
duPont REGISTRY Group's deployment of real-time AI analytics for luxury vehicles demonstrates a scalable model for dynamic pricing, authentication, and market forecasting of high-value collectibles. This approach directly translates to luxury retail for limited editions, vintage items, and exclusive collections.
Google's TimesFM: The Zero-Shot Time Series Model That Works Without Training
Google has open-sourced TimesFM, a foundation model for time series forecasting that requires no training on specific datasets. Unlike traditional models, it can make predictions directly from historical data, potentially revolutionizing forecasting across industries.
AI Forecasters Revise AGI Timeline: Key Milestones Pulled Forward to 2029-2030 After Recent Model Progress
A significant update from AI forecasters indicates key AGI milestones have been pulled forward, with the median prediction for AGI arrival shifting from 2032 to 2029-2030. This revision follows rapid progress in recent model capabilities, particularly in reasoning and tool use.
AI Offensive Cybersecurity Capabilities Double Every 5.7 Months, Matching METR's AI Timelines
An independent analysis extends METR's AI capability timeline research to offensive cybersecurity, finding a 5.7-month doubling time. Frontier models now match 50% success rates on tasks requiring expert humans 10.5 hours.
Why Luxury Brands Are Shunning AI in Favor of Handcraft
An article highlights a perceived tension in the luxury sector, where some brands are reportedly avoiding AI to preserve the authenticity and heritage of handcraft. This stance presents a core strategic challenge: balancing technological efficiency with brand identity.
The Self Driving Portfolio: Agentic Architecture for Institutional Asset Management
Researchers propose an 'agentic strategic asset allocation pipeline' using ~50 specialized AI agents to forecast markets, construct portfolios, and self-improve. The system is governed by a traditional Investment Policy Statement, aiming to automate high-level asset management.
Kering Shake-Up Reaches Jeweller DoDo as CEO Exits
The Business of Fashion reports that Kering's internal shake-up has extended to its jewellery subsidiary DoDo, resulting in the exit of its CEO. This indicates the luxury conglomerate's restructuring efforts are intensifying across its brand portfolio.
Google's AI Infrastructure Strategy: What Retail Leaders Should Watch in 2026
Google's evolving AI infrastructure and compute strategy, including data center investments and model compression techniques, will directly impact how retail brands deploy and scale AI applications by 2026. The company's focus on efficiency and real-time capabilities signals a shift toward more accessible, powerful retail AI tools.
Fenty Beauty Launches 'Rose Amber' AI Advisor on WhatsApp, Joining L'Oréal in Chat-Based Commerce Push
Fenty Beauty has launched 'Rose Amber,' a conversational AI advisor on WhatsApp for product recommendations and tutorials. This reflects a broader industry shift, with L'Oréal already generating over 20% of its DTC sales in Brazil via WhatsApp and planning a 2026 expansion of its own AI tool to the platform.
Google Cloud's Vertex AI Experiments Solves the 'Lost Model' Problem in ML Development
A Google Cloud team recounts losing their best-performing model after training 47 versions, highlighting a common MLops failure. They detail how Vertex AI Experiments provides systematic tracking to prevent this.
AI Researcher Kimmonismus Predicts AGI Within 6-12 Months, Widespread Worker Replacement in 1-2 Years
Independent AI researcher Kimmonismus predicts AGI will arrive within 6-12 months, with widespread worker displacement following in 1-2 years. The forecast, shared on X, adds to a growing chorus of near-term AGI predictions from industry figures.
The Cognitive Divergence: AI Context Windows Expand as Human Attention Declines, Creating a Delegation Feedback Loop
A new arXiv paper documents the exponential growth of AI context windows (512 tokens in 2017 to 2M in 2026) alongside a measured decline in human sustained-attention capacity. It introduces the 'Delegation Feedback Loop' hypothesis, where easier AI delegation may further erode human cognitive practice. This is a foundational study on human-AI interaction dynamics.
Uber Acquires Luxury Chauffeur Service Blacklane to Expand Executive Travel Business
Uber has acquired the luxury chauffeur booking platform Blacklane, which operates in over 500 cities across 60+ countries. This strategic move directly expands Uber's footprint in the high-end, executive travel segment.
Deloitte Report: The Future of Commerce is Agentic Shopping in Asia Pacific
Deloitte has published a report on 'Agentic Shopping' in Asia Pacific, framing AI agents as the next major commerce paradigm. This signals a strategic shift from passive recommendation engines to proactive, autonomous shopping assistants.
The Future of Production ML Is an 'Ugly Hybrid' of Deep Learning, Classic ML, and Rules
A technical article argues that the most effective production machine learning systems are not pure deep learning or classic ML, but pragmatic hybrids combining embeddings, boosted trees, rules, and human review. This reflects a maturing, engineering-first approach to deploying AI.
Ex-OpenAI Researcher Daniel Kokotajlo Puts 70% Probability on AI-Caused Human Extinction by 2029
Former OpenAI governance researcher Daniel Kokotajlo publicly estimates a 70% chance of AI leading to human extinction within approximately five years. The claim, made in a recent interview, adds a stark numerical prediction to ongoing AI safety debates.
The Socratic Model: A Hierarchical AI Architecture That Delegates to Specialists
A new research paper proposes a 3B-parameter hierarchical AI system called the Socratic Model. Instead of one monolithic LLM, it uses a lightweight router to classify queries and delegate to specialized expert models, outperforming a generalist baseline on mixed math/logic tasks.
REWE Expands Pick&Go Cashierless Store Test to Seventh Location in Hanover
German retailer REWE has launched its seventh Pick&Go cashierless convenience store test location in Hanover. This expansion signals continued investment in frictionless retail technology, a space where AI-powered computer vision and sensor fusion are critical.