{"product_id":"time-series-forecasting-using-foundation-models","title":"Time Series Forecasting Using Foundation Models","description":"Make accurate time series predictions with powerful pretrained foundation models!\u003cbr\u003e\u003cbr\u003eYou 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.\u003cbr\u003e\u003cbr\u003eIn 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 modelsTime 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.\u003cbr\u003e\u003cbr\u003eAbout the technology\u003cbr\u003e\u003cbr\u003eTime-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.\u003cbr\u003e\u003cbr\u003eAbout the book\u003cbr\u003e\u003cbr\u003eTime 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.\u003cbr\u003e\u003cbr\u003eWhat's inside- How large time models work- Zero-shot forecasting on custom datasets- Fine-tuning and evaluating foundation modelsAbout the reader\u003cbr\u003e\u003cbr\u003eFor data scientists and machine learning engineers familiar with the basics of time series forecasting theory. Examples in Python.\u003cbr\u003e\u003cbr\u003eAbout the author\u003cbr\u003e\u003cbr\u003eMarco Peixeiro builds cutting-edge open-source forecasting Python libraries at Nixtla. He is the author of Time Series Forecasting in Python.\u003cbr\u003e\u003cbr\u003eTable of Contents\u003cbr\u003e\u003cbr\u003ePart 1\u003cbr\u003e1 Understanding foundation models\u003cbr\u003e2 Building a foundation model\u003cbr\u003ePart 2\u003cbr\u003e3 Forecasting with TimeGPT\u003cbr\u003e4 Zero-shot probabilistic forecasting with Lag-Llama\u003cbr\u003e5 Learning the language of time with Chronos\u003cbr\u003e6 Moirai: A universal forecasting transformer\u003cbr\u003e7 Deterministic forecasting with TimesFM\u003cbr\u003ePart 3\u003cbr\u003e8 Forecasting as a language task\u003cbr\u003e9 Reprogramming an LLM for forecasting\u003cbr\u003ePart 4\u003cbr\u003e10 Capstone project: Forecasting daily visits to a blog","brand":"Buchserien.de","offers":[{"title":"Neuware","offer_id":55098557006162,"sku":"9781633435896","price":57.5,"currency_code":"EUR","in_stock":true}],"url":"https:\/\/www.buchserien.de\/products\/time-series-forecasting-using-foundation-models","provider":"Buchserien.de","version":"1.0","type":"link"}