A Hybrid Data Mining Framework for Financial Time-Series Prediction
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Nowadays stock price forecasting is a challenging task because of inherent unpredictability, noise, and non-linear dynamic stock time-series data. This study proposes a hybrid data mining model that integrates wavelet decomposition, LSTM model, and Light gradient Boosting Machine (LightGBM) to increase the accuracy and stability. Wavelet transformation works to disintegrate the original price series into trend and residual components, that enables effective reduction of noises and separation of features.
