Time series forecasting admits a wide range of applications from signal processing, pattern recognition and weather forecasting to mathematical finance, to name only a few. Machine learning techniques for time-series forecasting have been widely studied [1, 2]. The traditional recurrent approaches towards sequence modeling tasks [1, 2] have been recently challenged by convolutional network architectures [3-6]. Latter compete in the categories speed and precision and regularly outperform conventional recurrent approaches such as LSTM's, GRU's or RNN's [7-12]. In particular, those convolutional architectures may overcome the deficiencies of recurrent networks to handle long and multi-scale sequences with increased receptive fields [3, 13, 14]. For time sequences of images convolutional LSTM's aim to combine the best of both worlds [15, 16]. In this work we present a novel approach to utilize convolutional neural networks for image sequence as well as general sequence forecasting tasks. In contrast to the recent serge in causal "dilated" convolutional networks [3-6, 13, 14, 17, 18] our approach is closer in spirit to non-casual architectures [19-22]. However, our architecture distinguishes itself by its composite design for time series forecasting, see fig.