7 stories tagged Forecasting
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.