Addressing challenges in time series forecasting: a comprehensive comparison of machine learning techniques

The explosion of time series (TS) data, driven by advancements in technology, necessitates sophisticated analytical methods. Modern management systems increasingly rely on analyzing this data, highlighting the importance of efficient processing techniques. State-of-the-art machine learning (ML) appr...

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Главные авторы:	Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Taylor & Francis Group 2026-04-01
Серии:	Statistical Theory and Related Fields
Предметы:	Time series (TS) machine learning (ML) regression task outliers missing data
Online-ссылка:	https://www.tandfonline.com/doi/10.1080/24754269.2026.2633813
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