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Deep Neural Networks for Time Series Forecasting
 Kayabasi, Yigit Mertol ; Pilát, Martin (advisor) ; Neruda, Roman (referee)
Time series forecasting is a task of both academic and pragmatic interest. Although it has been long dominated by qualitative methods and simple quan- titative methods, machine learning and deep learning algorithms in modelling temporal data has become more common, but the progress is still far from the progress in typical machine learning tasks like computer vision or natural lan- guage processing. Recurrent neural networks are the most natural choice for modelling sequential data, but training them is tricky especially to learn from long sequences. Recently a divergence from back propagation Reservoir Comput- ing paradigm has started to draw attention with the performance of the models arising from it in this kind of tasks. They proved to be a good option partic- ularly for modelling rather more chaotic systems. In this thesis we will explore and compare these two families of neural networks regarding their performance and implementation. 1
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