Distribution regression on sequential data describes the task of learning a function from a group of time series to a single scalar target. I will present a generic framework, based on the expected signature, which enables to compactly summarise a cloud of time series and make decisions on it. I will then demonstrate empirically how this framework achieves state-of-the-art performance on both synthetic and real-world examples from thermodynamics, mathematical finance and agricultural science.