IEEE

Resilience-Oriented Daily Peak Load Forecasting on Utility Open Data Using a Dual-Stream Seasonal ARIMA and Kolmogorov-Arnold Network

Abstract: Short-term load forecasting (STLF) is a core input to unit commitment and operating-reserve scheduling. However, average error metrics can mask tail ...
Scientific Research Publishing

Chen, L. and Zhang, D. (2022) Forecasting High-Speed Rail Passenger Demand with Hybrid ARIMA and Machine Learning Models. Transportation Research Part A: Policy and Practice ...

ABSTRACT: Flow in high-speed rail (HSR) hubs serves as a “barometer” for factor mobility within urban agglomerations, and its accurate forecasting is crucial for capacity allocation and emergency ...
GitHub

Supply Chain Forecasting System

A) Retail/E-commerce inventory (forecasting product demand for stores or online sales) B) Manufacturing raw materials (forecasting material needs for production) C) Distribution/logistics (forecasting ...
TCTMD

TCT 50: Regional and Temporal Trends in Mortality Among Older Adults With Atrial Fibrillation and Cardiac or Vascular Device Complications in the U.S. (1999–2023), With ARIMA ...

Receive the the latest news, research, and presentations from major meetings right to your inbox. TCTMD ® is produced by the Cardiovascular Research Foundation ® (CRF). CRF ® is committed to igniting ...
Scientific Research Publishing

Kontopoulou, V.I., Panagopoulos, A.D., Kakkos, I. and Matsopoulos, G.K. (2023) A Review of ARIMA Vs. Machine Learning Approaches for Time Series Forecasting in Data Driven ...

ABSTRACT: Accurate forecasting of diesel prices is essential for Malaysia, where transportation, agriculture, and industry are highly dependent on this fuel. This study investigates the effectiveness ...
International Monetary Fund

A Python Package to Assist Macroframework Forecasting: Concepts and Examples

In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining ...
NPR

Hurricane science has come far since Katrina. That progress is now at risk

In the 20 years since Hurricane Katrina devastated New Orleans, hurricane scientists have made great strides toward understanding how climate change influences tropical cyclones, at the same time as ...