Wavelet Transforms in Financial Time Series Analysis: A Review on Stock Price Prediction
DOI:
https://doi.org/10.65080/mijai.v1.CM2601105002Keywords:
Wavelet transform, time series data, machine learning, stock price prediction, denoise data, stock price trendAbstract
Introduction: Stock price prediction is notoriously tricky due to data complexities such as non-stationarity and noise.
Methods: This literature survey was conducted in order to systematically evaluate how wavelet transforms enhance forecasting by improving data preprocessing for machine learning models.
Results: The review of hybrid wavelet-ML architectures (e.g., LSTMs, Transformers) finds that they consistently outperform standalone models by leveraging wavelets' powerful denoising and multi-scale feature-extraction capabilities. Despite their effectiveness, persistent challenges include parameter optimization, model interpretability, and overfitting.
Conclusion: Future research priorities should include adaptive wavelet techniques and the development of more robust, transparent hybrid systems for practical financial applications.
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