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商品描述
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 book Time Series Forecasting Using Foundation Models takes a practical approach to solving time series problems using pre-trained foundation models. In this easy-to-follow guide, you'll learn instantly-useful skills like zero-shot forecasting and informing pretrained models with your own data. You'll put theory into practice immediately as you start building your own small-scale foundation model to illustrate pretraining, transfer learning, and fine-tuning in chapter 2. Next, you'll dive into cutting-edge models like TimeGPT and Chronos and see how they can deliver zero-shot probabilistic forecasting, point forecasting, and more. You'll even find out how you can reprogram an LLM into a time-series forecaster. All the Python code and hands-on experiments run on a normal laptop. No high-performance GPU required! 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 is a seasoned data science instructor at Data Science with Marco, who works at Nixtla building cutting-edge open-source forecasting Python libraries. He is the author of Time Series Forecasting in Python. 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.
- 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 book Time Series Forecasting Using Foundation Models takes a practical approach to solving time series problems using pre-trained foundation models. In this easy-to-follow guide, you'll learn instantly-useful skills like zero-shot forecasting and informing pretrained models with your own data. You'll put theory into practice immediately as you start building your own small-scale foundation model to illustrate pretraining, transfer learning, and fine-tuning in chapter 2. Next, you'll dive into cutting-edge models like TimeGPT and Chronos and see how they can deliver zero-shot probabilistic forecasting, point forecasting, and more. You'll even find out how you can reprogram an LLM into a time-series forecaster. All the Python code and hands-on experiments run on a normal laptop. No high-performance GPU required! 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 is a seasoned data science instructor at Data Science with Marco, who works at Nixtla building cutting-edge open-source forecasting Python libraries. He is the author of Time Series Forecasting in Python. 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.
商品描述(中文翻譯)
使用強大的預訓練基礎模型進行準確的時間序列預測！
您不需要花費數週甚至數月的時間來編碼和訓練自己的時間序列預測模型。使用基礎模型的時間序列預測將向您展示如何使用靈活的預訓練模型進行準確的預測。 在使用基礎模型的時間序列預測中，您將發現： - 大型時間模型的內部運作- 在自定義數據集上的零樣本預測
- 微調基礎預測模型
- 評估大型時間模型 使用基礎模型的時間序列預測教您如何使用已在數十億數據點上預訓練的強大時間序列模型進行高效預測。您將欣賞那些展示您可以用這些驚人模型完成的實作範例。在這個過程中，您將學習時間序列基礎模型的運作方式、如何微調它們，以及如何使用自己的數據。 關於本書 使用基礎模型的時間序列預測採取實用的方法來解決使用預訓練基礎模型的時間序列問題。在這本易於跟隨的指南中，您將學到即時可用的技能，如零樣本預測和使用自己的數據來告知預訓練模型。您將立即將理論付諸實踐，開始構建自己的小型基礎模型，以說明第2章中的預訓練、轉移學習和微調。接下來，您將深入了解尖端模型，如TimeGPT和Chronos，並了解它們如何提供零樣本概率預測、點預測等。您甚至會發現如何將一個大型語言模型（LLM）重新編程為時間序列預測器。所有的Python代碼和實作實驗都可以在普通筆記本電腦上運行。不需要高性能的GPU！ 關於讀者 適合對時間序列預測理論基礎有一定了解的數據科學家和機器學習工程師。範例使用Python。 關於作者 Marco Peixeiro是Data Science with Marco的資深數據科學講師，並在Nixtla工作，構建尖端的開源預測Python庫。他是Python中的時間序列預測的作者。 購買印刷版書籍時，您將獲得Manning提供的免費電子書（PDF或ePub），以及訪問在線liveBook格式（及其AI助手，將以任何語言回答您的問題）的權限。
作者簡介
Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada's largest banks. He is an active contributor to Towards Data Science, an instructor on Udemy, and on YouTube in collaboration with freeCodeCamp.
作者簡介(中文翻譯)
Marco Peixeiro 是一位經驗豐富的資料科學講師，曾在加拿大最大的銀行之一擔任資料科學家。他是 Towards Data Science 的活躍貢獻者，也是 Udemy 的講師，並與 freeCodeCamp 在 YouTube 上合作。
