Stock-Market Prediction using Neural Networks for Multi-Output Regression in Python

This article showcases multi-output neural networks for time-series regression and demonstrates their use in the context of stock market forecasting. We will train and test a Keras neural network with ten output neurons using historical price quotes for Apple stock. … Continued

Feature Engineering for Multivariate Time Series Prediction with Python

Multivariate time series forecasting models often do not rely exclusively on historical time series data but use additional features such as moving averages or momentum indicators. The underlying assumption is that multiple variables increase the accuracy of a forecast by … Continued

Stock Market Prediction using Multivariate Time Series and Recurrent Neural Networks in Python

Time series forecasting has become a popular domain for applying deep learning technologies and recurrent neural networks in recent years. Regression models based on recurrent neural networks can recognize patterns in large data sets and thus make more accurate predictions … Continued

Stock Market Prediction – Adjusting Time Series Prediction Intervals in Python

In a previous article on stock market forecasting, we have created a forecast for the S&P500 stock market index using a neural network and Python. The prediction interval used in this previous article was a single day. However, many time-series … Continued

Stock Market Prediction using Univariate Time Series Models based on Recurrent Neural Networks with Python

Predicting the price of financial assets has fascinated researchers and analysts for many decades. While the traditional prediction methods of technical analysis and fundamental analysis are still widely used, interest is increasingly turning to machine-generated predictions based on deep learning. … Continued