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Time Series Analysis in Python

4.6+
18 reviews
Intermediate

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

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4 Hours17 Videos59 Exercises
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Course Description

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.
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In the following Tracks

Time Series with Python

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  1. 1

    Correlation and Autocorrelation

    Free

    In this chapter you'll be introduced to the ideas of correlation and autocorrelation for time series. Correlation describes the relationship between two time series and autocorrelation describes the relationship of a time series with its past values.

    Play Chapter Now
    Introduction to Course
    50 xp
    A "Thin" Application of Time Series
    100 xp
    Merging Time Series With Different Dates
    100 xp
    Correlation of Two Time Series
    50 xp
    Correlation of Stocks and Bonds
    100 xp
    Flying Saucers Aren't Correlated to Flying Markets
    100 xp
    Simple Linear Regression
    50 xp
    Looking at a Regression's R-Squared
    100 xp
    Match Correlation with Regression Output
    50 xp
    Autocorrelation
    50 xp
    A Popular Strategy Using Autocorrelation
    100 xp
    Are Interest Rates Autocorrelated?
    100 xp
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In the following Tracks

Time Series with Python

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Datasets

Financial time series datasetsUFO sightingsNew York temperature data

Collaborators

Collaborator's avatar
Lore Dirick
Collaborator's avatar
Nick Solomon
Rob Reider HeadshotRob Reider

Consultant at Quantopian and Adjunct Professor at NYU

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Don’t just take our word for it

*4.6
from 18 reviews
78%
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  • Hsing-chuan H.
    3 months

    Understandable!

  • Nadia H.
    11 months

    I liked the course because it was well explained and covered a nice range of topics. Not too difficult, not too easy, but nicely flowing along. I also think the explanations were clear and the exercises were good. I am happy with this course and recommended it to my colleagues.

  • Georgi P.
    about 1 year

    👍

  • Francis K.
    over 1 year

    Very clear, concise and useful

  • Greg T.
    over 1 year

    I've studied time series for many years, but this course showed me much better, scientific and statistical ways to analyze and predict time series data. Having completed this course, I'm now confident enough to do these kinds of analysis at my job and also for personal projects.

"Understandable!"

Hsing-chuan H.

"I liked the course because it was well explained and covered a nice range of topics. Not too difficult, not too easy, but nicely flowing along. I also think the explanations were clear and the exercises were good. I am happy with this course and recommended it to my colleagues."

Nadia H.

"👍"

Georgi P.

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