Stock Market Price Prediction using Neural Prophet with Deep Neural Network

TL;DR

This paper introduces a Neural Prophet combined with a Deep Neural Network (NP-DNN) for stock price prediction, utilizing data normalization and imputation to improve forecast accuracy, achieving over 99% accuracy.

Contribution

The novel integration of Neural Prophet with Deep Neural Networks for enhanced stock market prediction accuracy is presented, addressing limitations of traditional statistical methods.

Findings

Achieved 99.21% prediction accuracy.

Effective use of Z-score normalization and missing value imputation.

Improved nonlinear pattern learning with MLP.

Abstract

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.