Abstract: This paper presents a sector-specific employment forecasting framework that integrates deep learning with heterogeneous labor market data, including job postings and macroeconomic indicators ...

AI market forecasting is reshaping how organizations anticipate demand, risk, and opportunity by processing massive volumes of structured and unstructured data in near real time. Modern systems ingest ...

MIAMI — The Atlantic hurricane season, which draws to an official close on Sunday, fulfilled forecasts it would be an active year. There were 13 named storms and three Category 5 hurricanes. But, for ...

Corporate income tax (CIT) collections are among the most difficult revenues to forecast—even with adequate staffing, comprehensive data, and a stable tax design. In practice, forecasting units ...

At its annual delegate meeting on Sept. 20, the Minnesota Association of Professional Employees (MAPE) awarded its annual Outstanding Union Service Award to the organizing committee of MAPE’s ...

Google's DeepMind just released WeatherNext 2, a new version of its AI weather prediction model. The company promises that it "delivers more efficient, more accurate and higher-resolution global ...

Google DeepMind and Google Research today announced WeatherNext 2 as its “most advanced and efficient forecasting model.” Notably, it’s helping power forecasts in Google’s consumer apps, including ...

Hi, thank you for your work on TTM and FlowState. I am a contributor to Darts, a popular time series forecasting Python library. Recently, I have started to implement some foundation models such as ...

This project provides a modern, well-structured implementation of hierarchical time series forecasting methods. It supports various forecasting algorithms (ARIMA, Prophet, LSTM) and reconciliation ...

Will the world still need economists in 2030? With skin in the game, we certainly hope so — but artificial intelligence is already transforming economic analysis. In our own work — and in a review of ...