Time series forecasting has been a widely explored task of great importance in many
applications. However, it is common that real-world time series data are recorded in a short …

Multivariate time series data comprises various channels of variables. The multivariate
forecasting models need to capture the relationship between the channels to accurately …

Rule extraction (RE) from recurrent neural networks (RNNs) refers to finding models of the
underlying RNN, typically in the form of finite state machines, that mimic the network to a …

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence
learning. They are less commonly applied to financial time series predictions, yet inherently …

The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD
can be predicted. The price data is sourced from the Bitcoin Price Index. The task is …

There is a substantial number of telerobotics and teleoperation applications ranging from
space operations, ground/aerial robotics, drive-by-wire systems to medical interventions …

The application of electroencephalogram (EEG)-based emotion recognition (ER) to the brain–
computer interface (BCI) has become increasingly popular over the past decade. Emotion …

Machine learning methods trained on raw numerical time series data exhibit fundamental
limitations such as a high sensitivity to the hyper parameters and even to the initialization of …

In the financial domain, risk modeling and profit generation heavily rely on the sophisticated
and intricate stock movement prediction task. Stock forecasting is complex, given the …

Code flaws or vulnerabilities are prevalent in software systems and can potentially cause a
variety of problems including deadlock, hacking, information loss and system failure. A …