Please help me to paraphrase this , để tránh AI plagiarism: Transformer-based models, which employ self-attention processes rather than recurrent or convolutional approaches, represent a paradigm shift in sequential modelling. These Transformers have achieved cutting-edge performance by accurately modelling long-range relationships and capturing complicated patterns throughout large time series datasets.
While individual models have demonstrated promising results, there is a lack of comprehensive comparative studies that evaluate advanced deep learning techniques (such as LSTM, GRU, Hybrid, and Transformer) alongside traditional methods (including SVR, ARIMA, MLP, and standard RNN), particularly in the context of significant financial indices like the SP 500.
This thesis addresses this gap by conducting a systematic comparative analysis of the forecasting performance of advanced deep learning methods in conjunction with traditional techniques specifically for the SP 500 index. By examining the relative strengths and weaknesses of each model, this research aims to provide valuable insights that can inform both academic investigations and practical applications in the domain of financial forecasting.
Transformer-based models, leveraging self-attention instead of traditional recurrent or convolutional mechanisms, mark a transformative change in the field of sequential modelling. By effectively capturing intricate patterns and long-range dependencies in extensive time series datasets, Transformers have set new performance benchmarks.
Despite individual models showing potential, there's a noticeable gap in extensive comparative research contrasting advanced deep learning approaches (like LSTM, GRU, Hybrid, and Transformers) with conventional methods (such as SVR, ARIMA, MLP, and classic RNN), especially concerning major financial benchmarks like the SP 500.
This thesis aims to fill this void by performing a detailed comparative study of how both modern deep learning and traditional forecasting techniques perform on the SP 500 index. Through exploring the advantages and limitations of each approach, the research seeks to offer valuable insights that can guide both academic studies and practical applications in financial forecasting.
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