Ethan Rosenthal: Time frame combination of for scikit-learn people at large | PyData Ny right now

This talk will frame the topic of time series forecasting in the language of machine learning. This framing will be used to introduce the skits library which provides a scikit-learn-compatible API for fitting and forecasting time series models using supervised machine learning. Finally, a real-world deployment of skits involving thousands of forecasts per hour will be demonstrated.

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Marios Michailidis & Mathias Muller, Aqua.aje that in fact Time frame Group by having Water Driverless Ojai through Aqua World New york

This video was recorded in San Francisco on February 5th, 2019.

Slides from the session can be viewed here:

Driverless AI is’s latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code, and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy.

Driverless AI is now equipped with time-series functionality. Time series helps forecast sales, predict industrial machine failure and more. With the time series capability in Driverless AI, directly addresses some of the most pressing concerns of organizations across industries for use cases such as transactional data in capital markets, in retail to track in-store and online sales, and in manufacturing with sensor data to improve supply chain or predictive maintenance.

Bio: Marios Michailidis is a Competitive Data Scientist at He holds a Bsc in accounting Finance from the University of Macedonia in Greece, an Msc in Risk Management from the University of Southampton and a PhD in machine learning at from UCL . He has worked in both marketing and credit sectors in the UK Market and has led many analytics’ projects with various themes including: acquisition, retention, recommenders, fraud detection, portfolio optimization and more. He is the creator of KazAnova, a freeware GUI for credit scoring and data mining 100% made in Java as well as is the creator of StackNet Meta-Modelling Framework. In his spare time he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular data competition platform. He currently ranks 3rd.

Bio: A Kaggle Grandmaster and a Data Scientist at, Mathias Müller holds an AI and ML focused diploma (eq. M.Sc.) in computer science from Humboldt University in Berlin. During his studies, he keenly worked on computer vision in the context of bio-inspired visual navigation of autonomous flying quadrocopters. Prior to, he as a machine learning engineer for FSD Fahrzeugsystemdaten GmbH in the automotive sector. His stint with Kaggle was a chance encounter as he stumbled upon the data competition platform while looking for a more ML-focused platform as compared to TopCoder. This is where he entered his first predictive modeling competition and climbed up the ladder to be a Grandmaster. He is an active contributor to XGBoost and is working on Driverless AI with

In the first half of this video, Jo-Fai will share his joyful (yet sometimes very painful) Kaggle experience since joining the data mining competition platform. Coming from a rather traditional engineering background, data science was once like a complete myth to him. Joe will explain why participating in Kaggle is one of the most effective ways to kick-start a data science career. He will also explain how he used H2O for two Kaggle competitions: Rossmann Store Sales (2015) and Santander Product Recommendation (2016).

View slides here:


Jo-fai (or Joe) Chow is a data scientist at Before joining H2O, he was in the business intelligence team at Virgin Media in UK where he developed data products to enable quick and smart business decisions. He also worked remotely for Domino Data Lab in US as a data science evangelist promoting products via blogging and giving talks at meetups. Joe has a background in water engineering. Before his data science journey, he was an EngD research engineer at STREAM Industrial Doctorate Centre working on machine learning techniques for drainage design optimization. Prior to that, he was an asset management consultant specialized in data mining and constrained optimization for the utilities sector in UK and abroad. He also holds a MSc in Environmental Management and a BEng in Civil Engineering.

Abhishek’s Talk

In the second part of this talk, Abhishek will present his research in applying deep learning for time series prediction. He is focused on applying these new methods in the field of astronomy to light curves.

View slides here:


Abhishek Malali is a Master’s of Engineering student at Harvard University specializing in Computational Sciences. He focuses on applying machine learning research to time series applications. Currently he is working on time series prediction on irregular time series using deep learning architectures.