Machine Learning Applied
Stock Market Prediction is about developing models to predict the further development of a financial asset such as, a stock. There are different approaches to this. In classical machine learning, a prediction model is trained on the basis of historical data in the hope that this will provide information about future developments.
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
In this article, we will develop a Twitter bot in Python that will generate automated trading signals. Our bot will pull real-time price data on various cryptocurrencies (Bitcoin, Ethereum, Doge, etc.) from the crypto exchange Gate.io and analyze it using … Continued
Streaming price data lays the foundation for many exciting machine learning applications in finance, such as generating trading signals for buy and sell orders. This tutorial shows how to stream bitcoin price quotes in real-time via a public API and … Continued
time series components
Line charts that use color as an overlay are a great way to visualize complex trends in time series data. The color of the line plot illustrates changes in another variable. Such color-coded line plots have recently become popular among … Continued
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
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