Time series forecasting is different from other machine learning problems.
The key difference is the fixed sequence of observations and the constraints and additional structure this provides.
In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and specialized methods for time series forecasting.
Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.
Technical Details About the Book
- PDF format Ebook.
- 8 parts, 34 chapters, 367 pages.
- 28 step-by-step tutorial lessons.
- 3 end-to-end projects.
- 181 Python (.py) files.
Click here to buy this self-published book.
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