What does this research mean for the field?

A hybrid forecasting framework combining ARIMA, a novel QuadGRU, and multi-head attention significantly improves financial market prediction accuracy and stability across different volatility regimes compared to conventional models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop a volatility-adaptive hybrid forecasting model combining statistical and deep temporal architectures to effectively capture structural breaks and market regime changes.

August 29, 2026Scientific ReportsOpen Access

Volatility-adaptive temporal learning framework using hybrid ARIMA-QuadGRU attention for multi-regime financial forecasting

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Authors

APAmit Pimpalkar RSRachna SomkunwarDr. D. Y. Patil Medical College, Hospital and Research Centre NSNilesh ShelkeSymbiosis International University

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Implication

Machine learning study demonstrates enhanced directional accuracy in financial time-series forecasting, indicating improved adaptability across volatile market regimes.

Key Points

To develop a volatility-adaptive hybrid forecasting model combining statistical and deep temporal architectures to effectively capture structural breaks and market regime changes.
Analyzed 15 years of daily NIFTY-50 index market price data (2010–2025) using rolling window validation against baseline ARIMA, LSTM, and GRU models.
Preprocessed data via Isolation Forest anomaly masking, technical indicators, gradient boosting machine feature ranking, principal component analysis, and Granger causality testing.
Combined ARIMA for short-term linear sequences with an 8-head attention QuadGRU network to handle nonlinear temporal dependencies.
Achieved a normalized MSE of 0.012 ± 0.003 (14,850 index-point MSE), an RMSE of 0.110 ± 0.014 (121.8 index points), and an MAE of 0.084 ± 0.011 (93.1 index points).
Demonstrated an overall directional accuracy of 87.5 ± 2.1%, showing sustained stability particularly during high-volatility regimes.

Cite This Study

Pimpalkar et al. (2026) studied this question.

synapsesocial.com/papers/6a9299338e5d7d1fc0c110bc https://doi.org/10.1038/s41598-026-68122-4

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Implication

Machine learning study demonstrates enhanced directional accuracy in financial time-series forecasting, indicating improved adaptability across volatile market regimes.

Volatility-adaptive temporal learning framework using hybrid ARIMA-QuadGRU attention for multi-regime financial forecasting

Discussion

Member takes

Implication

Key Points

Cite This Study

Volatility-adaptive temporal learning framework using hybrid ARIMA-QuadGRU attention for multi-regime financial forecasting

Discussion

Member takes

Implication

Key Points

Cite This Study

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