Stock market price prediction is a significant interdisciplinary research domain that depends at the intersection of finance, statistics, and economics. Forecasting Accurately predicting stock prices has always been a focal point for various researchers. However, existing statistical approaches for time-series prediction often fail to effectively forecast the probability range of future stock prices. Hence, to solve this problem, the Neural Prophet with a Deep Neural Network (NP-DNN) is proposed to predict stock market prices. The preprocessing technique used in this research is Z-score normalization, which normalizes stock price data by removing scale differences, making patterns easier to detect. Missing value imputation fills gaps in historical data, enhancing the models use of complete information for more accurate predictions. The Multi-Layer Perceptron (MLP) learns complex nonlinear relationships among stock market prices and extracts hidden patterns from the input data, thereby creating meaningful feature representations for better prediction accuracy. The proposed NP-DNN model achieved an accuracy of 99.21% compared with other approaches using the Fused Large Language Model. Keywords: deep neural network, forecasting stock prices, multi-layer perceptron, neural prophet, stock market price prediction.

翻译：股票市场价格预测是一个重要的跨学科研究领域，涉及金融学、统计学和经济学的交叉。准确预测股票价格一直是众多研究者的关注焦点。然而，现有的时间序列预测统计方法往往无法有效预测未来股票价格的概率区间。因此，为解决这一问题，本文提出了一种结合深度神经网络的Neural Prophet模型（NP-DNN）用于股票市场价格预测。本研究采用的预处理技术为Z-score归一化，该方法通过消除尺度差异对股票价格数据进行标准化处理，使数据模式更易于识别。缺失值填补技术可补充历史数据中的空白，增强模型利用完整信息进行更准确预测的能力。多层感知机（MLP）能够学习股票市场价格之间复杂的非线性关系，并从输入数据中提取隐藏模式，从而构建有意义的特征表示以提高预测精度。与其他采用融合大语言模型的方法相比，所提出的NP-DNN模型达到了99.21%的预测准确率。关键词：深度神经网络，股票价格预测，多层感知机，神经先知，股票市场价格预测。