Applying Recurrent Neural Networks to Time-Series Analysis in Big Data for Decision Support

The burgeoning field of big data has necessitated advanced analytical techniques capable of extracting valuable insights from complex time-series datasets. This research explores the application of Recurrent Neural Networks (RNN s), specifically Long Short-Term Memory (LSTM) models, in the context of time-series analysis for enhancing decision support systems. The study begins with a comprehensive literature review, examining the evolution of time-series analysis and the integration of deep learning methodologies. A theoretical framework is established, detailing the mathematical principles of time-series and the architecture of LSTM networks, followed by a methodological approach to model training and validation. Preliminary results are presented through insightful visualizations, showcasing the predictive capabilities of the RNN models against traditional forecasting methods. The paper delves into an advanced data analysis phase, assessing model performance across various metrics and datasets. The discussion section synthesizes the findings, evaluating the effectiveness of RNN s in decision support and contemplating future research avenues. The paper concludes by highlighting the transformative potential of deep learning in business analytics and strategic decision-making.