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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מידע ביבליוגרפי

Principais autores:	Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Taylor & Francis Group 2026-04-01
סדרה:	Statistical Theory and Related Fields
נושאים:	Time series (TS) machine learning (ML) regression task outliers missing data
גישה מקוונת:	https://www.tandfonline.com/doi/10.1080/24754269.2026.2633813
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