talk-data.com

Make accurate time series predictions with powerful pretrained foundation models! You don’t need to spend weeks—or even months—coding and training your own models for time series forecasting. Time Series Forecasting Using Foundation Models shows you how to make accurate predictions using flexible pretrained models. In Time Series Forecasting Using Foundation Models you will discover: The inner workings of large time models Zero-shot forecasting on custom datasets Fine-tuning foundation forecasting models Evaluating large time models Time Series Forecasting Using Foundation Models teaches you how to do efficient forecasting using powerful time series models that have already been pretrained on billions of data points. You’ll appreciate the hands-on examples that show you what you can accomplish with these amazing models. Along the way, you’ll learn how time series foundation models work, how to fine-tune them, and how to use them with your own data. About the Technology Time-series forecasting is the art of analyzing historical, time-stamped data to predict future outcomes. Foundational time series models like TimeGPT and Chronos, pre-trained on billions of data points, can now effectively augment or replace painstakingly-built custom time-series models. About the Book Time Series Forecasting Using Foundation Models explores the architecture of large time models and shows you how to use them to generate fast, accurate predictions. You’ll learn to fine-tune time models on your own data, execute zero-shot probabilistic forecasting, point forecasting, and more. You’ll even find out how to reprogram an LLM into a time series forecaster—all following examples that will run on an ordinary laptop. What's Inside How large time models work Zero-shot forecasting on custom datasets Fine-tuning and evaluating foundation models About the Reader For data scientists and machine learning engineers familiar with the basics of time series forecasting theory. Examples in Python. About the Author Marco Peixeiro builds cutting-edge open-source forecasting Python libraries at Nixtla. He is the author of Time Series Forecasting in Python. Quotes Clear and hands-on, featuring both theory and easy-to-follow examples. - Eryk Lewinson, Author of Python for Finance Cookbook Bridges the gap between classical forecasting methods and the new developments in the foundational models. A fantastic resource. - Juan Orduz, PyMC Labs A foundational guide to forecasting’s next chapter. - Tyler Blume, daybreak An immensely practical introduction to forecasting using foundation models. - Stephan Kolassa, SAP Switzerland

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.

​ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q02ZkDV90 • Code of conduct: https://odsc.com/code-of-conduct/

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.

​ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q02ZkDV90 • Code of conduct: https://odsc.com/code-of-conduct/

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.

​ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q02ZkDV90 • Code of conduct: https://odsc.com/code-of-conduct/

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.

​ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q02ZkDV90 • Code of conduct: https://odsc.com/code-of-conduct/

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.

​ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q02ZkDV90 • Code of conduct: https://odsc.com/code-of-conduct/