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Time Series Forecasting Using Foundation Models
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ISBN10: 163343589X
ISBN13: 9781633435896
Publisher: Manning Publications
Published: Dec 16 2025
Pages: 256
Weight: 0.71
Language: English
ISBN13: 9781633435896
Publisher: Manning Publications
Published: Dec 16 2025
Pages: 256
Weight: 0.71
Language: English
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. Table of Contents Part 1
1 Understanding foundation models
2 Building a foundation model
Part 2
3 Forecasting with TimeGPT
4 Zero-shot probabilistic forecasting with Lag-Llama
5 Learning the language of time with Chronos
6 Moirai: A universal forecasting transformer
7 Deterministic forecasting with TimesFM
Part 3
8 Forecasting as a language task
9 Reprogramming an LLM for forecasting
Part 4
10 Capstone project: Forecasting daily visits to a blog Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
- 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. Table of Contents Part 1
1 Understanding foundation models
2 Building a foundation model
Part 2
3 Forecasting with TimeGPT
4 Zero-shot probabilistic forecasting with Lag-Llama
5 Learning the language of time with Chronos
6 Moirai: A universal forecasting transformer
7 Deterministic forecasting with TimesFM
Part 3
8 Forecasting as a language task
9 Reprogramming an LLM for forecasting
Part 4
10 Capstone project: Forecasting daily visits to a blog Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
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