Join PyData NYC on April 10th at 6:30 pm for a workshop night with Jorn Mossel and Thomas J. Fan. Please bring your laptops to code along.
š Food, drinks, and venue sponsored by Microsoft
Agenda:
Introduction to Time Series Forecasting: 30 mins
Speaker: Jorn Mossel
Time series problems, such as sales forecasting, energy demand, and peak server load predictions, are widespread in data science, yet often overlooked in introductory machine learning courses. This tutorial offers a brief introduction to time series, highlighting how they differ from other machine learning problems. We then demonstrate both traditional and more advanced time series techniques through explicit examples in Python. We conclude by giving an overview of some of the latest developments and available Python packages for time series analysis.
Jorn is a Physics Ph.D., a former quant on Wall Street, and a senior data scientist working on energy demand forecasting.
Time Series EDA with STUMPY: 25 minutes
Speaker: Thomas J. Fan
STUMPY is a robust and scalable Python library for computing a matrix profile, which can create valuable insights about our time series. STUMPY is built with Numba, which parallelizes computing on CPUs and accelerates it with GPUs. In this talk, we learn about the matrix profile and the various methods to compute it. Afterwards, we explore how to use the matrix profile in applications such as pattern discovery, anomaly detection, semantic segmentation, and time series chains.
Thomas J. Fan is a senior machine learning engineer at
Union.ai and a maintainer for scikit-learn. At scikit-learn, he led the development of DataFrame interoperability and GPU support through PyTorch. Previously, as a researcher at Columbia University, Thomas collaborated with NASA to automate machine learning workflows.
Networking
Connect with fellow data enthusiasts, professionals, and community leaders. Build meaningful connections and forge collaborations.
----------------------------------------------------------------
RSVP is required; please note that walk-ins will not be accepted.
----------------------------------------------------------------
The building requires a government-issued photo ID for entrance.
This, and all PyData NYC events, is an all-level event. Newcomers and beginners are welcome.
This, and all NumFOCUS-affiliated events and spaces, both in-person and online, are governed by a Code of Conduct. More at
https://pydata.org/code-of-conduct/This event may be recorded.
Time Series Forecasting š
Host
Apr 10, 10:30 PM - Apr 11, 12:30 AM (GMT)
Midtown (New York)
30 attendees
Fullš Food, drinks, and venue sponsored by Microsoft
Agenda:
Introduction to Time Series Forecasting: 30 mins
Speaker: Jorn Mossel
Time series problems, such as sales forecasting, energy demand, and peak server load predictions, are widespread in data science, yet often overlooked in introductory machine learning courses. This tutorial offers a brief introduction to time series, highlighting how they differ from other machine learning problems. We then demonstrate both traditional and more advanced time series techniques through explicit examples in Python. We conclude by giving an overview of some of the latest developments and available Python packages for time series analysis.
Jorn is a Physics Ph.D., a former quant on Wall Street, and a senior data scientist working on energy demand forecasting.
Time Series EDA with STUMPY: 25 minutes
Speaker: Thomas J. Fan
STUMPY is a robust and scalable Python library for computing a matrix profile, which can create valuable insights about our time series. STUMPY is built with Numba, which parallelizes computing on CPUs and accelerates it with GPUs. In this talk, we learn about the matrix profile and the various methods to compute it. Afterwards, we explore how to use the matrix profile in applications such as pattern discovery, anomaly detection, semantic segmentation, and time series chains.
Thomas J. Fan is a senior machine learning engineer at Union.ai and a maintainer for scikit-learn. At scikit-learn, he led the development of DataFrame interoperability and GPU support through PyTorch. Previously, as a researcher at Columbia University, Thomas collaborated with NASA to automate machine learning workflows.
Networking
Connect with fellow data enthusiasts, professionals, and community leaders. Build meaningful connections and forge collaborations.
----------------------------------------------------------------
RSVP is required; please note that walk-ins will not be accepted.
----------------------------------------------------------------
The building requires a government-issued photo ID for entrance.
This, and all PyData NYC events, is an all-level event. Newcomers and beginners are welcome.
This, and all NumFOCUS-affiliated events and spaces, both in-person and online, are governed by a Code of Conduct. More at https://pydata.org/code-of-conduct/
This event may be recorded.