mloning

You can get prescription inlets for some of them, including these ones.

Location: Europe

Remote: hybrid or remote

Willing to relocate: yes

Technologies: Python, Kubernetes, C#, SQL, MLFlow, Azure, Terraform, bash, scikit-learn, pytorch

Résumé/CV: upon request

Email: www.linkedin.com/in/mloning

Machine learning engineer

We're actually interfacing tsfresh. Unifying ML with time series is perhaps better understood in terms of the different learning tasks (e.g. time series classification/regression/clustering, forecasting, time series annotation) and their relations (applying algorithms for one task to help solve another).

We're interfacing statsmodels and pmdarima for the implementation of the ARIMA model. I believe that you can persist models in statsmodels without saving the whole training data.

sktime is a toolbox with the goal to support multiple models and composition techniques, Prophet is a particular model. We're working on interfacing it so that you can call it using our API.