用深度神经网络增强神经预言家模型，提升股市价格预测准确率。
Stock Market Price Prediction using Neural Prophet with Deep Neural Network
- 结合Z-score归一化与缺失值填充，优化数据预处理。
- 基于MLP捕捉非线性关系，实现99.21%的预测准确率。
- 适合金融量化研究者与时间序列预测方向读者。
股票市场价格预测是金融、统计与经济学交叉的重要研究领域。准确预测股价一直是研究重点，但现有统计方法在预测未来股价概率范围方面表现不佳。为此，本文提出一种融合深度神经网络的神经预言家模型（NP-DNN），用于股票市场价格预测。研究采用Z-score标准化对股价数据进行预处理，消除量纲差异，便于模式识别；通过缺失值插补填补历史数据空缺，提升模型对完整信息的利用能力。多层感知机（MLP）学习股价间的复杂非线性关系，从输入数据中提取有意义的特征表示，从而提高预测精度。实验表明，该模型相比其他方法在融合大型语言模型的测试中达到99.21%的准确率。
原文摘要 · Abstract (English)
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.
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