Course
Time Series Analysis in Python
IntermediateSkill Level
Updated 08/2024Start Course for Free
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PythonProbability & Statistics4 hr17 videos59 Exercises4,850 XP69,178Statement of Accomplishment
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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
Manipulating Time Series Data in Python1
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.
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
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Enroll NowFAQs
Why use time series analysis?
We use time series analysis to understand the causes of systemic patterns or trends seen over time. By visualizing data, individuals or organizations can identify relevant trends and their causes.
How do you analyze time series data?
To analyze time series data, you need to record data consistently, meaning you have data points spread over a set period. You can use various tools to plot and visualize this time series data, including Python.
How do you create time series data in Python?
There are several ways you can create time series data in Python. You can use the pandas_datareader library to access real-world data, use the requests library, or even use tools such as NumPy to create synthetic data.
How do you visualize time series data in Python?
There are several ways you can express data in Python, including with libraries such as Matplotlib and Seaborn. You can learn more from our course on visualizing time series data in Python.
Is Python good for forecasting?
Yes, Python has several libraries and tools for performing time series forecasting, which can help you build things like ARMA models using relatively straightforward code.
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