14 stories tagged Forecasting
Allen Institute for AI's HiRO-ACEv2 model simulates extreme weather at unprecedented 3km resolution. It's 100x faster and 10x cheaper than traditional methods, making high-quality weather forecasting more accessible.
Microsoft researchers have developed a cost-effective machine learning tool that improves weather forecast accuracy for the two to six-week range, a period where traditional physics-based and AI models typically become unreliable. The tool benefits farmers, emergency responders, and other industries reliant on long-term predictions.
Researchers introduced Alignment Forecasting, a method to predict AI misalignment before training by analyzing fine-tuning datasets for failure modes like deception or sycophancy, complementing post-hoc audits.
IBM Research has released Granite Time Series PatchTST-FM-r2, a new AI model for time series forecasting that achieves state-of-the-art accuracy and is available under a commercial-friendly license, making it accessible for business use.
Researchers released EXAONE Forecast for Finance (EXAONE Finance), a financial time series foundation model (TSFM) tailored to financial forecasting. It handles partially observed inputs and uses an efficient computational backbone to reduce quadratic cost, achieving strong zero-shot performance on financial data.
Google DeepMind's WeatherNext AI model can now predict cyclone paths and intensities up to 3 days earlier than traditional methods. This breakthrough could significantly improve disaster preparedness and response times.
Researchers from the University of Miami and NOAA developed CycloneNet, an AI model that forecasts tropical cyclone paths and intensities with 95% accuracy, outperforming traditional physics-based models and promising to improve disaster preparedness.
Researchers developed a way to measure the quality of written explanations using AI. They analyzed over 55,000 predictions from a forecasting tournament and found patterns that help assess how well people justify their decisions. This could improve how we evaluate expert opinions and AI reasoning alike.
Researchers developed a new AI framework called SERAF that enhances time series forecasting by combining historical patterns with semantic context to address non-stationarity. This approach could make predictions more accurate for real-world applications like stock markets and weather forecasting.
Scientists developed a new way to predict how AI systems will behave, bypassing traditional explanation methods. This could make AI more trustworthy by showing users what to expect.
Researchers introduced TimeClaw, an agentic framework that improves time series analysis by integrating rich contextual information and supporting end-to-end workflows. This could make tools for forecasting and pattern analysis more accurate and practical for real-world applications.
Researchers developed a method called GITCO to improve AI forecasting by cleaning up the input data instead of changing the model itself. This could make AI predictions more accurate without needing to retrain the model.
WindBorne uses AI and weather balloons to gather data, improving forecasts. Its unique approach is now more accurate than traditional government systems.
Researchers introduce BTF-2, a benchmark with 1,417 pastcasting questions, to analyze AI forecasters' reasoning. The study identifies accuracy differences as small as 0.004 Brier score and builds a more accurate composite forecaster.