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.
Multivariate time series predictions and especially stock market forecasts pose challenging machine learning problems. Unlike univariate forecasting models, multivariate models do not rely exclusively on historical time series data, but use additional functions that are often developed from the time … Continued
Time series prediction has become a major domain for the application of machine learning and more specifically recurrent neural networks. Well-designed multivariate prediction models are now able to recognize patterns in large amounts of data, allowing them to make more … Continued
Time series forecasting models Evaluating the performance of forecasting models is important and a crucial step in their development. This is especially the case for time series forecasting models. Compared to classification models, time series predictions cannot easily be divided … Continued
Forecasting the price of financial assets has fascinated researchers and analysts for many decades. While traditional prediction methods of technical analysis and fundamental analysis are still widely used, interest is now increasingly steering towards automated predictions with machine learning. A … Continued