Learn How to Use Python for Time Series Analysis

From stock prices to climate data, you can find time series data in a wide variety of domains. Having the skills to work with such data effectively is an increasingly important skill for data scientists. This course will introduce you to time series analysis in Python.

After learning what a time series is, you'll explore several time series models, ranging from autoregressive and moving average models to cointegration models. Along the way, you'll learn how to estimate, forecast, and simulate these models using statistical libraries in Python.

You'll see numerous examples of how these models are used, with a particular emphasis on applications in finance.

Discover How to Use Time Series Methods

You’ll start by covering the fundamentals of time series data, as well as simple linear regression. You’ll cover concepts of correlation and autocorrelation and how they apply to time series data before exploring some simple time series models, such as white noise and a random walk. Next, you’ll explore how autoregressive (AR) models are used for time series data to predict current values and how moving average models can combine with AR models to produce powerful ARMA models.

Finally, you’ll look at how to use cointegration models to model two series jointly before looking at a real-life case study.

Explore Python Models and Libraries for Time Series Analysis By the end of this course, you’ll understand how time series analysis in Python works. You’ll know about some of the models, methods, and libraries that can assist you with the process and will know how to choose the appropriate ones for your own analysis.

This course is part of a wider Time Series with Python Track, which provides a set of five courses to help you master this data science skill.

Match Correlation with Regression Output

Time Series Analysis in Python

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