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Join Chicago Python for a deep dive into Time Series Analysis using Facebook Prophet, InfluxDB, and STUMPY! This event will be Live Streamed to our YouTube Channel: https://www.youtube.com/watch?v=gjcweY4YoG0

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AGENDA
6:00 - Broadcast starts on YouTube
8:30 - Estimated end time

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Our talks:

Facebook Prophet and TimeSeries Databases
by Anais Dotis-Georgiou

Data collection is only half of the battle. The other half is being able to easily perform data analysis. FB Prophet aims to make time series forecasting simple and fast. In this talk, we’ll learn how to make a univariate time series prediction with Prophet and InfluxDB, a time series database.

Bio:
Anais Dotis-Georgiou is a developer advocate at InfluxData with a passion for making data beautiful using data analytics, AI, and machine learning. She takes the data that she collects and does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty.

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Using Facebook Prophet in Production
by Ray Buhr

Last year I had a project that requested generating unique forecasts for hundreds to thousands of customers. Naturally, I didn't want to spend hours on each individually and looked for something that could dynamically and automatically detect seasonality and changepoints. The prophet package from Facebook does just this. It's the pareto efficient solution for this type of problem -- you get 80% of need for 20% of the work. I want to talk about how I built a system for using fbprophet in production to generate time series forecasts in parallel as a scheduled job. I also want to talk about where fbprophet worked out great vs where it falls a little flat to help you decide whether it's useful for your next project.

Bio:
Ray Buhr has been working in data analysis and data science roles for almost 10 years now at 4 different companies of varying size. Time series modeling has popped up in every single one of them.

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STUMPY: A Powerful and Scalable Library for Modern Time Series Analysis
by Sean Law

Traditional time series analysis techniques have found success in a variety of data mining tasks. However, they often require years of experience to master and the recent development of straightforward, easy-to-use analysis tools has been lacking. We address these needs with STUMPY, a scientific Python library that implements a novel yet intuitive approach for discovering patterns, anomalies, and other insights from any time series data. This presentation will cover the necessary background needed to follow the live interactive demo, requires no prior experience, and promises a simple, powerful, and scalable time series analysis library that will complement your current toolset.

Bio:
Sean Law is a senior applied scientific researcher and data scientist currently working with a multi-talented Exploration Lab team (formerly, Advanced Technology) and serves as an advisor on an enterprise A.I. Council at TD Ameritrade. He has experience producing cutting edge methodologies, building high-performance predictive models, and developing rapid prototypes.

Additionally, Sean co-organizes the monthly PyData Ann Arbor data science meetup and is also the creator and core maintainer of STUMPY, a powerful and scalable open source Python library that can be used for a variety of time series data mining tasks.

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