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.
