TimeSeriesPrediction

Predicting the future development of time series is of interest in different areas of computational biology. The time series in biological systems exhibit challenging characteristics such as chaotic or stochastic behavior. In this report, time series forecasting and generation is done on several kinds of biological time series using deep neural networks. We compare classical multi layer perceptrons with recurrent and convolutional architectures. In our evaluation, we identify challenges and limitations of neural networks for these tasks, and compare different architectures of deep neural networks with regard to their performance. We conclude that the configuration of the optimization algorithm has strong impact on the results, independent of the architecture. The nature of the noise in the data has also strong influence. We also show that a generation of stochastic time series is not possible using deterministic neural networks, because only the mean of the probability distribution can be estimated.