Time Series Analysis, Stock Market Prices, Prediction, Autoregressive Model, Long Short-Term Memory Network (LSTM)
Abstract
With the increasing complexity of financial markets and the rapid growth of data volumes, accurately predicting stock prices has become crucial for ensuring market stability and guiding investment decisions. Although traditional time series analysis models have been widely applied, they still face research gaps in prediction accuracy due to the high nonlinearity and dynamics of the market. This paper introduces the application of time series analysis methods in stock market price prediction within the financial industry. This paper aims to enhance prediction accuracy and timeliness. The study provides a detailed overview of the advantages of both traditional models and modern deep learning techniques. The latter offers a new perspective for prediction through its capabilities in nonlinear modeling and capturing long-term dependencies. Representative stocks in the financial industry are selected for empirical analysis, demonstrating the performance of models that combine time series analysis with deep learning techniques in terms of prediction accuracy and stability. Meanwhile, the paper points out current research deficiencies, such as model optimization, improvement in generalization ability, and interdisciplinary integration, as future directions. With the backdrop of big data, it is hoped that through the rational integration of time series analysis and deep learning techniques, stronger support can be provided for financial market stability and scientific decision-making.
