"多层级模型分析在股票价格预测中的应用研究"。

The research paper "Data Mining Stock Price Prediction.pdf" delves into the complexities of predicting stock prices, highlighting the challenges posed by the nonlinearity and randomness of stock prices, as well as the constant changes in the economic and political landscape at home and abroad. The authors emphasize the limitations of relying on a single analyzing model for accurate and effective stock price prediction and propose a multi-model analysis approach to enhance forecast accuracy. The research focuses on applying different models for analyzing stock prices at various levels. The authors utilize exponential smoothing and ARIMA to forecast stock prices in the New Energy sector, specifically the stock index prediction. The use of SPSS Clem as a tool for this analysis underscores the importance of leveraging advanced software to handle the complexities of stock price prediction. The study acknowledges the inherent challenges in predicting stock prices and highlights the need for a comprehensive and multifaceted approach to address these challenges. By incorporating multiple models and leveraging advanced analytical tools, the research aims to improve the effectiveness of stock price forecasts. Overall, the paper underscores the complexities of stock price prediction and the need for a sophisticated and comprehensive approach to effectively forecast stock prices in an ever-changing economic and political environment. The utilization of different models and advanced analytical tools such as SPSS Clem demonstrates the commitment to enhancing the accuracy and effectiveness of stock price prediction in the New Energy sector and beyond.