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.
ARIMA (Auto Regressive Integrated Moving Average) is a useful statistical modelling technique for time series forecasting. Compared to machine learning, ARIMA is a classical modeling technique that is very strong especially when the time series to be analyzed follows a clear … Continued
The recent rise in the price of Bitcoin and other cryptocurrencies has also led to increased interest in visualizing price trends. In particular, price charts that use color as an overlay have a particular appeal. Such colorful price charts not … Continued
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
Since the sudden emergence of COVID-19, financial markets have experienced turbulent times. A sharp slump in the stock markets was followed by a sharp rise from mid-2020 onwards, bringing stock indices back to record highs by the end of the … 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