Virtual event: "Time Series Mastery"

This event has PAID and FREE Passes. More info you may find here - https://lu.ma/7ochmq77
Pre-Registration via lu.ma is REQUIRED.

Time series forecasting is more than just predicting future trends - it’s a critical skill for industries ranging from finance to healthcare, retail, and beyond. Join us for a one-day virtual event packed with expert-led workshops designed to equip you with the latest AI-driven and classical forecasting techniques.

## ​What’s on the agenda?

12.00 pm ET - Talk - Jeff Tackes, Global Head of Forecasting at Kraft Heinz and Hamed Alikhani PhD- Data Scientist at Kraft Heinz - 30 min
12.30 pm ET - Talk - Marco Peixeiro, Applied AI Scientist Nixtla - 30 min
1.00 pm ET - Workshop - John Mount, PhD Principal Consultant, Win Vector LLC - 1 h
2.00 pm ET - Training - Jeffrey Yau, Former Global Head of Data Science and Engineering at Amazon Music - 2 h

Talk#1 details:
Topic: Optimizing Forecast Stability and Accuracy

In this talk, we introduce a novel approach leveraging genetic algorithms to optimize both forecast stability and accuracy, creating a dynamically weighted ensemble that balances these competing objectives and delivering better accuracy than any single base model. By incorporating past model performance into our evolutionary framework, we iteratively evolve an ensemble that minimizes large forecast swings while maintaining or improving overall accuracy. We demonstrate how this method systematically adjusts model weights based on historical deviations and performance metrics, solving a key business challenge.

Talk#2 details:
Topic: State of Foundation Models For Time Series Forecasting

First, we explore the core concepts of foundation models, such as pretraining, transfer learning and fine-tuning. Second, we take a look at the advantages and disadvantages of foundation models in time series forecasting. While they can speed up the modeling and inference process, they might also not be the best solution for a particular project, meaning that we must still have a certain expertise to use them correctly and compare them with other methods. Then, we explore some of the major contributions to the field, including TimeGPT, Chronos, Moirai and TimesFM. We quickly discover their architectures, their capabilities and their limitations. Finally, we see TimeGPT in action to demonstrate how a foundation model can be used and how it compares to traditional methods.

Training details:
Topic: Unlocking the Future with AI-Driven Time Series Forcasting

Time series forecasting is the science of predicting future events based on historical data, a practice with applications that permeate our daily lives. Consider demand and inventory planning, where forecasting enables businesses to anticipate customer needs, ensuring optimal product availability while minimizing costs.

Workshop details:
Topic: ​Forecasting the Future Using Time Series

​Time series forecasting remains a specialty topic specializing in "predicting the future". Because of this, you really want to use a package that is tuned for your use case, and specialized to deal with the difficulties inherent in time series forecasting. Speaker will share a simplified problem notation that helps you to survey available solution offerings, and succeed with time series packages in R and Python.

Additionally, with Time Series event Paid Pass you will have Ai+ Premium Annual Subscription - https://hubs.li/H0Zycsf0
​It will give access to dozens on-demand sessions, Gen AI&LLMs cerification, 5-week AI Bootcamp, extra discounts to attend ODSC conferences and more.

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