Time series analysis presents unique challenges in the field of machine learning. Dealing with randomly observed time series is still difficult with standard methods as timestamps are irregularly spaced. Long Range Dependent time series are notoriously hard to analyze as they can lead to the discovery of correlation where there is none. The scale of data sets now offers unique opportunities in terms of training deeper, more complex models, but requires a lot of modifications to standard approaches which were designed for a single machine to run fast. Temgine aims at offering a query optimization based solution that leverages the duality between time and frequency domain in time series in order to provide estimators for randomly observed data, efficiently erase Long Range Dependencies and mitigate the communication burden between computation nodes.