Implement Time Series Analysis, Forecasting and Prediction with Tensorflow 2.0

Course Overview
Hi everyone. My name is Chase DeHan, and welcome my course, Implementing Time Series Analysis, Forecasting, and Prediction with TensorFlow 2.0. I currently lead data science at Tesorio and hold a PhD in Economics from the University of Utah. In this course, we're going to cover the neural network approach to time series forecasting and prediction using TensorFlow. TensorFlow provides alternative ways to generate these time series models that aren't classical, statistical methods. This makes them easier to implement, and in many, but not all cases, able to generate better performing forecasts. Some of the major topics that we will cover include the basics of time series analysis, proper time series cross validation and windowing, and finally, the ability to generate univariate and time series forecasts using covariate methods. By the end of this course, you'll know how to build time series models with a variety of different models and have the foundation to experiment building your own. Before beginning the course, we should be familiar with Python and TensorFlow. This course will use many of the modeling concepts you've learned in the past with a twist of applying the models to time series data. I hope you'll join me on this journey with the Time Series Analysis, Forecasting, and Prediction in TensorFlow course, at Pluralsight.