The Time Series Informer Model for Stock Market Prediction

This investigation examines the efficacy of the Time Series Informer (TSI) architecture in forecasting stock prices, positioning it as a pivotal instrument within business intelligence (BI) paradigms. Amid the escalating intricacy and nonlinear dynamics inherent to financial markets, deep learning frameworks have emerged as preeminent modalities for delineating sequential dependencies. Employing a comprehensive historical dataset of stock prices sourced from Google, the present analysis juxtaposes the TSI with the Long Short-Term Memory (LSTM) model. Performance is rigorously benchmarked through dual quantitative indices: The Root Mean Square Error (RMSE) and the Pearson correlation coefficient. Supplementary assessments encompass convergence trajectories, computational parsimony, and temporal overheads associated with model training. Empirical findings substantiate the superior predictive fidelity and correlative fidelity of TSI vis-à-vis LSTM, underscoring its adeptness at encapsulating protracted temporal interdependencies in financial chronologies. Visualization of convergence profiles evinces accelerated and more resilient optimization dynamics for TSI. Collectively, this multifaceted juxtaposition elucidates the model's viability for pragmatic stock market prognostication, thereby illuminating the transformative prospective of advanced neural architectures in fortifying strategic business intelligence infrastructures.