Stock Market Price Prediction using Neural Prophet with Deep Neural Network
Image generated by Gemini AI
A new model, Neural Prophet with Deep Neural Network (NP-DNN), has been introduced to enhance stock market price predictions, achieving an impressive accuracy of 99.21%. By employing Z-score normalization and addressing missing data, the model leverages Multi-Layer Perceptron (MLP) to capture complex patterns, outperforming existing methods.
Neural Prophet with Deep Neural Network Achieves High Accuracy in Stock Market Price Prediction
A new model integrating Neural Prophet with a Deep Neural Network (NP-DNN) has achieved an accuracy of 99.21% in predicting stock market prices. This advancement could reshape financial forecasting by addressing limitations found in traditional statistical methods.
The NP-DNN model employs Z-score normalization and missing value imputation to enhance predictive accuracy. At its core, the model uses a Multi-Layer Perceptron (MLP) that learns complex nonlinear relationships among stock prices, extracting hidden patterns to improve predictions.
Comparative analysis indicates that NP-DNN outperforms other forecasting approaches, highlighting the potential of deep learning techniques in stock market predictions.
Related Topics:
📰 Original Source: https://arxiv.org/abs/2601.05202v1
All rights and credit belong to the original publisher.