Implement Time Series Analysis, Forecasting and Prediction with Tensorflow 2.0

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Chase DeHan

1:05:39

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01. Course Overview

01. Course Overview.mp4

01:23

02. Understanding Time Series Data

02. Introduction.mp4

01:32

03. What Is a Time Series.mp4

02:44

04. Evaluation Metrics.mp4

03:05

05. Using a Hold Out.mp4

02:13

06. Load Data.mp4

01:42

07. Basic Time Series Windows.mp4

03:13

03. Building a Baseline Model

08. Introduction.mp4

01:16

09. Data Preparation.mp4

01:44

10. Split Data.mp4

02:06

11. WindowGenerator Class.mp4

04:05

12. Additional Methods in WindowGenerator.mp4

01:25

13. More Methods.mp4

01:56

14. Single Step Window.mp4

02:00

15. Baseline Model Class.mp4

03:08

16. Linear Model.mp4

02:59

04. Utilizing Neural Networks

17. Introduction.mp4

01:23

18. Compile and Fit.mp4

02:53

19. Dense Model.mp4

02:09

20. Convolutional Model.mp4

02:58

21. Recurrent Neural Networks.mp4

04:16

05. Expanding the Modeling Approach

22. Introduction.mp4

00:56

23. Predict Multiple Outputs.mp4

02:54

24. RNN on Multiple Outputs.mp4

01:27

25. Predict Multiple Periods.mp4

02:44

26. Linear Model and Multiple Periods.mp4

01:58

27. Dense Model and Multiple Periods.mp4

01:02

28. CNNs and Multiple Outputs.mp4

01:56

29. LSTM and Multiple Outputs.mp4

02:32

Description

Time series analysis is one of the more difficult and confusing aspects of data science. This course will teach you how to use TensorFlow with time series data and generate high performing forecasts and predictions.

What You'll Learn?

Time series predictions are difficult and the rise of neural networks and TensorFlow has made generating highly performant machine learning models possible. In this course, Implement Time Series Analysis, Forecasting, and Prediction with TensorFlow 2.0, you’ll learn how to build models with multiple TensorFlow model types and be able to select the highest performing model. First, you’ll explore time series cross validation and how to create a baseline. Next, you’ll discover how to use neural networks on a single step ahead process. Finally, you’ll learn how to expand the modeling technique to predict multiple time periods in advance along with generating multiple simultaneous predictions on different series. When you’re finished with this course, you’ll have the skills and knowledge of TensorFlow needed to build models for good time series predictions.

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Machine Learning

TensorFlow

Time Series Analysis

Chase DeHan

Instructor's Courses

Chase is currently Lead Data Scientist at Tesorio and formerly was an Assistant Professor of Finance and Economics at the University of South Carolina Upstate. He holds a BS, MS, and PhD, all in Economics, from the University of Utah. Prior to graduate school, Chase served two combat tours to Iraq with the US Marine Corps and competed in the 2010 Winter Olympic Trials in Bobsled. Chase is passionate about building automated machine learning systems and is a regular speaker at academic and practitioner conferences. He lives in Salt Lake City and is an avid skier, mountain biker, and coffee junkie.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.