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 Course

A "Thin" Application of Time Series

Merging Time Series With Different Dates

Correlation of Two Time Series

Correlation of Stocks and Bonds

Flying Saucers Aren't Correlated to Flying Markets

Simple Linear Regression

Looking at a Regression's R-Squared

Match Correlation with Regression Output

Autocorrelation

A Popular Strategy Using Autocorrelation

Are Interest Rates Autocorrelated?

Correlation and Autocorrelation

In this chapter you'll learn about some simple time series models. These include white noise and a random walk.

Autocorrelation Function

Taxing Exercise: Compute the ACF

Are We Confident This Stock is Mean Reverting?

White Noise

Can't Forecast White Noise

Random Walk

Generate a Random Walk

Get the Drift

Are Stock Prices a Random Walk?

How About Stock Returns?

Stationarity

Is it Stationary?

Seasonal Adjustment During Tax Season

Some Simple Time Series

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.

Describe AR Model

Simulate AR(1) Time Series

Compare the ACF for Several AR Time Series

Match AR Model with ACF

Estimating and Forecasting AR Model

Estimating an AR Model

Forecasting with an AR Model

Let's Forecast Interest Rates

Compare AR Model with Random Walk

Choosing the Right Model

Estimate Order of Model: PACF

Estimate Order of Model: Information Criteria

Autoregressive (AR) 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.

Describe Model

Simulate MA(1) Time Series

Compute the ACF for Several MA Time Series

Match ACF with MA Model

Estimation and Forecasting an MA Model

Estimating an MA Model

Forecasting with MA Model

ARMA models

High Frequency Stock Prices

More Data Cleaning: Missing Data

Applying an MA Model

Equivalence of AR(1) and MA(infinity)

Moving Average (MA) and ARMA Models

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.

Cointegration Models

A Dog on a Leash? (Part 1)

A Dog on a Leash? (Part 2)

Are Bitcoin and Ethereum Cointegrated?

Case Study: Climate Change

Is Temperature a Random Walk (with Drift)?

Getting "Warmed" Up: Look at Autocorrelations

Which ARMA Model is Best?

Don't Throw Out That Winter Coat Yet

Congratulations

Putting It All Together

Financial time series datasets

UFO sightings

New York temperature data

From stock prices to climate data, time series data are found in a wide variety of domains, and being able to effectively work with such data is an increasingly important skill for data scientists. This course will introduce you to time series analysis in Python. After learning about what a time series is, you'll learn about 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.

<h2>Learn How to Use Python for Time Series Analysis </h2>
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.
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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. 
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You'll see numerous examples of how these models are used, with a particular emphasis on applications in finance.
<br><br>
<h2>Discover How to Use Time Series Methods </h2>
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. 
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Finally, you’ll look at how to use cointegration models to model two series jointly before looking at a real-life case study. 
<br><br>
</h2>Explore Python Models and Libraries for Time Series Analysis</h2>
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. 
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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. 


Manipulating Time Series Data in Python

Learn the basics of time series analysis in Python using several models, including autoregressive, moving average, and cointegration.

Time Series Analysis in Python

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

Time Series in Python

Likely to Recommend

Estimating an AR Model

“Time Series Analysis in Python”

Exercise instructions

Hands-on interactive exercise

Time Series Analysis in Python

Chapter 1: Correlation and Autocorrelation

Chapter 2: Some Simple Time Series

Chapter 3: Autoregressive (AR) Models

Chapter 4: Moving Average (MA) and ARMA Models

Chapter 5: Putting It All Together

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