$37 USD

Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.

In this new Ebook written in the friendly Machine Learning Mastery style that you’re used to, skip the math and jump straight to getting results.

With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.

About this Ebook:

• Read on all devices: PDF format Ebook, no DRM.
• Tons of tutorials: 5 parts, 25 step-by-step lessons, 575 pages.
• Real-world projects: 2 large end-to-end tutorial projects.
• Many datasets: Univariate, multivariate, multi-step, and more.
• Working code: 131 Python (.py) code files included.
  

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