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| Format: | Recurso digital |
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Zenodo
2022
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| Online Access: | https://doi.org/10.5281/zenodo.15665635 |
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Table of Contents:
- <h3>Finance integrates financial time series forecasting as its core component that enables traders and investors to construct decisions with help from historic dataset examination and predictive model applications. Statistical forecasting tools gained extensive usage before ML approaches because they serve as better solutions when handling complex and non-linear relationships. Various ML approaches are evaluated but this study applies specific focus to deep learning models and their LSTM networks and CNNs and explores hybrid models to boost predictive model outcomes. The section covers how financial time series forecasting suffers from overfitting and non-stationary along with noise-sensitive characteristics. The document offers experimental outcomes based on realistic financial forecasting conducted with various ML approaches.The period for developing new medications has become shorter after researchers implement their assessment methodology. The unique capability of ML to handle big complex volumes of information enables it to detect covert patterns beyond what traditional models can achieve. Using ensemble learning methods together with transfer learning aids the stability and enhances generalization capacity of the forecasting system. Organizations use ML-driven financial forecasting systems with timely data to both control risks and distribute assets and generate trading plans leading to superior financial results. The study deals with interpretability issues in ML models because financial analysts as well as regulatory members require complete understanding of model explanations before they will accept model applications.</h3>