Machine Learning Applied
A Recurrent Neural Network (RNN) is a specific form of a Neural Network. In a traditional feed-forward Neural Network all the information flows from left to right. In contrast, RNNs use Long-short-term memory (LSTM)-layers that allow them to recirculate output results back and forth through the network. In other words, LSTM layers have loops that enable the RNN to pass output values from one training instance to iterative instances. This allows them to learn patterns that occur on different levels of abstraction.
RNNs are particularly useful for analyzing time series. In this application domain, the LSTM layers allow the RNN to learn patterns that occur over different time periods, e.g., days and months, and potentially overlap. As a result, RNNs typically generate more accurate predictions in time-series analysis than traditional neural networks.
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
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
The evaluation of the prediction quality is a crucial step in the development of regression models. To evaluate regression models, we measure the deviation between predictions and actual values in numerical terms. Therefore, unlike classification models, regression models allow for … Continued
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