International Monetary Fund

Nowcasting Growth Using the Bayesian Structural Time Series Model

In light of recent global shocks and rising external volatility, there is a growing need to effectively monitor short-term economic fluctuations, especially in countries with limited access to ...

AI shows promise for flood forecasting and water security in data scarce regions

New research reveals that "foundation models" trained on vast, general time-series data may be able to forecast river flows accurately, even in regions with little or no local hydrological records.
Scientific Research Publishing

Data Infrastructure and the Evolution of Financial Analytics in the U.S. FinTech Ecosystem ()

Driven Financial Analysis, Time-Series Analysis, Financial Market Infrastructure Farzaliyeva, A. (2026) Data Infrastructure and the Evolution of Financial Analytics in the U.S. FinTech Ecosystem.
Communications of the ACM

Can AI Enable Efficient Scenario Planning?

AI scenario planning doesn’t attempt to be “right” in the traditional forecasting sense. Its real value lies in systematic ...
IEEE

Optimizing Deep Residual Networks for Short-Term Load Forecasting With Multidimensional Weather Data and Principal Component Analysis

Abstract: Short-term load forecasting (STLF) is essential for power system operations, supporting efficient grid management and resource planning. Deep Residual Networks (DRNs) have emerged as a ...
IEEE

UniMATS: A Unified Time Series Forecasting Model With Multi-Dimensional Attention Structure for Power Systems

Abstract: Due to the intrinsic complexity of time series forecasting within power systems, artificial intelligence has emerged as a promising pathway for predictive analytics. Although time series ...

Federal Reserve Research: Kalshi Prediction Markets Are Just as Good, if Not Better, Than Traditional Forecasting Methods

Researchers at the Federal Reserve recently published a paper on Kalshi's effectiveness in predicting certain economic ...