Kuldip Katiyar

Abstract Forecasting agricultural production in India is highly challenging due to non-stationary dynamics driven by policy factors and climate volatility. This study investigates the effectiveness of the fractal interpolation function (FIF) (with both variable and constant scaling factors) as a data augmentation technique to improve the prediction accuracy of long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) models.

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