A hybrid PCA-ICA and multi-level feature scaling framework with bidirectional LSTM-GRU architecture improves multivariate time series forecasting accuracy

Abstract Precise multivariate time series (MTS) forecasting, particularly in atmospheric applications like air quality monitoring, is still a challenging task because of high dimensionality, temporal correlations, and non-stationary interactions between features. The classical methods such as Auto R...

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Autori principali:	Yuvaraja Boddu, A. Manimaran, Jayanth Talabathula, M. Sucharitha
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Nature Portfolio 2026-05-01
Serie:	Scientific Reports
Soggetti:	Multivariate time series forecasting Hybrid feature scaling PCA-ICA dimensionality reduction Bi-LSTM-GRU neural network Component-wise inverse projection
Accesso online:	https://doi.org/10.1038/s41598-026-51868-2
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