Yuvaraja Boddu

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

May 17, 2026 |

By A. Manimaran, Yuvaraja Boddu, Jayanth Talabathula, M. Sucharitha

| Nature

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.

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

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