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You're reading from  Practical Guide to Applied Conformal Prediction in Python

Product typeBook
Published inDec 2023
PublisherPackt
ISBN-139781805122760
Edition1st Edition
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Author (1)
Valery Manokhin
Valery Manokhin
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Valery Manokhin

Valeriy Manokhin is the leading expert in the field of machine learning and Conformal Prediction. He holds a Ph.D.in Machine Learning from Royal Holloway, University of London. His doctoral work was supervised by the creator of Conformal Prediction, Vladimir Vovk, and focused on developing new methods for quantifying uncertainty in machine learning models. Valeriy has published extensively in leading machine learning journals, and his Ph.D. dissertation 'Machine Learning for Probabilistic Prediction' is read by thousands of people across the world. He is also the creator of "Awesome Conformal Prediction," the most popular resource and GitHub repository for all things Conformal Prediction.
Read more about Valery Manokhin

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UQ for time series and forecasting problems

UQ is not just a sophisticated addition to time series forecasting; it is a fundamental aspect that provides invaluable insights into the nature of the predictions. Let’s look at why it’s important and a brief history of its development.

The importance of UQ

UQ is a critical component of time series forecasting. While a forecast model may provide accurate predictions on average, understanding the uncertainty around those predictions is equally essential. There are several key reasons why properly quantifying uncertainty is vital for practical time series forecasting:

  • Risk assessment: In many domains, such as finance, healthcare, and environmental science, forecasting is closely linked with decision-making. Understanding the uncertainty in predictions aids in assessing potential risks, thus enabling informed decisions.
  • Model confidence: UQ provides an understanding of the confidence in each model’s predictions...
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Practical Guide to Applied Conformal Prediction in Python
Published in: Dec 2023Publisher: PacktISBN-13: 9781805122760

Author (1)

author image
Valery Manokhin

Valeriy Manokhin is the leading expert in the field of machine learning and Conformal Prediction. He holds a Ph.D.in Machine Learning from Royal Holloway, University of London. His doctoral work was supervised by the creator of Conformal Prediction, Vladimir Vovk, and focused on developing new methods for quantifying uncertainty in machine learning models. Valeriy has published extensively in leading machine learning journals, and his Ph.D. dissertation 'Machine Learning for Probabilistic Prediction' is read by thousands of people across the world. He is also the creator of "Awesome Conformal Prediction," the most popular resource and GitHub repository for all things Conformal Prediction.
Read more about Valery Manokhin