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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Autors principals:	Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang
Format:	Artigo
Idioma:	Inglês
Publicat:	Taylor & Francis Group 2026-04-01
Col·lecció:	Statistical Theory and Related Fields
Matèries:	Time series (TS) machine learning (ML) regression task outliers missing data
Accés en línia:	https://www.tandfonline.com/doi/10.1080/24754269.2026.2633813
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