Time series data is common in many settings including scientific and financial applications. In these applications, the amount of data is often very large. We seek to support pred...
Abstract. The aim of our research was to apply well-known data mining techniques (such as linear neural networks, multi-layered perceptrons, probabilistic neural networks, classifi...
Marcin Paprzycki, Ajith Abraham, Ruiyuan Guo, Srin...
In this paper, we improve the performance of intra prediction and simplify mode decision procedure at the same time. For these works, we apply a statistical learning method such a...
Prediction is an important task in robot motor control where it is used to gain feedback for a controller. With such a self-generated feedback, which is available before sensor rea...
We present an experiment comparing double exponential smoothing and Kalman filter-based predictive tracking algorithms with derivative free measurement models. Our results show t...