Machine Learning Applied
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
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
We can solve many time forecasting problems by looking at a single step into the future. However, some forecasting problems require us to understand how a signal will develop over a more extended period so that it is not enough … Continued
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