Economic forecasts tend to be unreliable. The news is fast paced, and predictions can quickly grow stale. But there’s still something to be gained.

The final, formatted version of the article will be published soon. The escalating frequency of infectious disease outbreaks underscores the urgent need for reliable forecasting systems to support ...

Abstract: Time series forecasting is widely used in finance, meteorology, and industrial systems. Although existing methods have made progress in modeling trend and periodicity, they still face ...

20 Superstars, two matches, one word... WarGames! The annual Survivor Series Premium Live Event returns on Saturday, November 29, when WWE takes over Petco Park and transforms the home of Major League ...

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

Code for our SIGKDD'25 paper: "BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models". The advent of universal time series forecasting models has revolutionized zero-shot ...

Scientists in China have developed a novel missingness-aware power forecasting method that leverages signal decomposition, multi-scale covariate interaction, and multi-domain collaborative transfer ...

Abstract: Traditional statistical time series forecasting models rely on model identification methods to identify the worthiest model variants to investigate; therefore, the model parameters change ...

1 School of Big Data and Software Engineering, Chongqing University, Chongqing, China 2 School of Computer and Information Science, Chongqing Normal University, Chongqing, China Introduction: Time ...