This code is the official PyTorch implementation of our AAAI'26 paper: APN: Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline. If you find this project helpful, please ...

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.

ABSTRACT: Purpose and Design: This research aims to develop an intelligent framework that integrates wage design, productivity performance, and profitability outcomes in state-owned enterprises (SOEs) ...

A unified foundation model for medical time series — pretrained on open access and ethics board-approved medical corpora — offers the potential to reduce annotation burdens, minimize model ...

Abstract: Irregular time series (ITS) data are widespread across healthcare, finance, and the Internet of Things (IoT), where accurate forecasting is crucial for applications such as disease ...

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...

The Federal Energy Regulatory Commission (FERC) on Sept. 18 advanced four reliability measures for the U.S. bulk power system (BPS), formalizing frameworks around supply chain risk, cloud computing ...

Influenza remains a significant public health challenge worldwide, necessitating robust forecasting models to facilitate timely interventions and resource allocation. The aim of this study was to ...

Abstract: Univariate time series, as a simple form of data, have been extensively studied in various fields. However, due to their limited information and high ...