This study represents an initial endeavor to harness the potential of the semantic space within the Twitter news flow to forecast financial anomalies. In pursuit of this objective, approximately two million entities were extracted from the news text disseminated by the most widely followed news...

A time series can often be characterized using machine learning techniques, which require feature vectors as input. The quality of the feature vectors reflects the accuracy of the utilized machine learning techniques. We propose a method for combining features extracted from two popular...