Time Series Forecasting Using Foundation by Marco Peixeiro (.PDF)

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File Size: 18.3 MB

Time Series Forecasting Using Foundation Models (Final Release) by Marco Peixeiro
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Overview: 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. 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! For data scientists and Machine Learning engineers familiar with the basics of time series forecasting theory. Examples in Python.
Genre: Non-Fiction > Tech & Devices

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