  Inference on time series data is a common requirement in many scientific
disciplines and internet of things (IoT) applications, yet there are few
resources available to domain scientists to easily, robustly, and repeatably
build such complex inference workflows: traditional statistical models of time
series are often too rigid to explain complex time domain behavior, while
popular machine learning packages require already-featurized dataset inputs.
Moreover, the software engineering tasks required to instantiate the
computational platform are daunting. cesium is an end-to-end time series
analysis framework, consisting of a Python library as well as a web front-end
interface, that allows researchers to featurize raw data and apply modern
machine learning techniques in a simple, reproducible, and extensible way.
Users can apply out-of-the-box feature engineering workflows as well as save
and replay their own analyses. Any steps taken in the front end can also be
exported to a Jupyter notebook, so users can iterate between possible models
within the front end and then fine-tune their analysis using the additional
capabilities of the back-end library. The open-source packages make us of many
use modern Python toolkits, including xarray, dask, Celery, Flask, and
scikit-learn.
