After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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.
Prerequisites
Curriculum
Course outline
1
Correlation and Autocorrelation
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.
- Introduction to Course50 XP
- A "Thin" Application of Time Series100 XP
- Merging Time Series With Different Dates100 XP
- Correlation of Two Time Series50 XP
- Correlation of Stocks and Bonds100 XP
- Flying Saucers Aren't Correlated to Flying Markets100 XP
- Simple Linear Regression50 XP
- Looking at a Regression's R-Squared100 XP
- Match Correlation with Regression Output50 XP
- Autocorrelation50 XP
- A Popular Strategy Using Autocorrelation100 XP
- Are Interest Rates Autocorrelated?100 XP
2
Some Simple Time Series
In this chapter you'll learn about some simple time series models. These include white noise and a random walk.
3
Autoregressive (AR) Models
In this chapter you'll learn about autoregressive, or AR, models for time series. These models use past values of the series to predict the current value.
4
Moving Average (MA) and ARMA Models
In this chapter you'll learn about another kind of model, the moving average, or MA, model. You will also see how to combine AR and MA models into a powerful ARMA model.
5
Putting It All Together
This chapter will show you how to model two series jointly using cointegration models. Then you'll wrap up with a case study where you look at a time series of temperature data from New York City.
Time Series Analysis in Python
Course
Complete
