Dive straight in and learn about the most important properties of time series. You'll learn about stationarity and how this is important for ARMA models. You'll learn how to test for stationarity by eye and with a standard statistical test. Finally, you'll learn the basic structure of ARMA models and use this to generate some ARMA data and fit an ARMA model.

Intro to time series and stationarity

Exploration

Train-test splits

Is it stationary

Making time series stationary

Augmented Dicky-Fuller

Taking the difference

Other tranforms

Intro to AR, MA and ARMA models

Model order

Generating ARMA data

Fitting Prelude

ARMA Models

What lies ahead in this chapter is you predicting what lies ahead in your data. You'll learn how to use the elegant statsmodels package to fit ARMA, ARIMA and ARMAX models.  Then you'll use your models to predict the uncertain future of stock prices!

Fitting time series models

Fitting AR and MA models

Fitting an ARMA model

Fitting an ARMAX model

Forecasting

Generating one-step-ahead predictions

Plotting one-step-ahead predictions

Generating dynamic forecasts

Plotting dynamic forecasts

Intro to ARIMA models

Differencing and fitting ARMA

Unrolling ARMA forecast

Fitting an ARIMA model

Choosing ARIMA model

Fitting the Future

In this chapter, you will become a modeler of discerning taste. You'll learn how to identify promising model orders from the data itself, then, once the most promising models have been trained, you'll learn how to choose the best model from this fitted selection. You'll also learn a great framework for structuring your time series projects.

Intro to ACF and PACF

AR or MA

Order of earthquakes

Intro to AIC and BIC

Searching over model order

Choosing order with AIC and BIC

AIC and BIC vs ACF and PACF

Model diagnostics

Mean absolute error

Diagnostic summary statistics

Plot diagnostics

Box-Jenkins method

Identification

Identification II

Estimation

Diagnostics

The Best of the Best Models

In this final chapter, you'll learn how to use seasonal ARIMA models to fit more complex data. You'll learn how to decompose this data into seasonal and non-seasonal parts and then you'll get the chance to utilize all your ARIMA tools on one last global forecast challenge.

Seasonal time series

Seasonal decompose

Seasonal ACF and PACF

SARIMA models

Fitting SARIMA models

Choosing SARIMA order

SARIMA vs ARIMA forecasts

Automation and saving

Automated model selection

Saving and updating models

SARIMA and Box-Jenkins

Multiplicative vs additive seasonality

SARIMA model diagnostics

SARIMA forecast

Congratulations!

Seasonal ARIMA Models

US Monthly Candy Production

Monthly Record of CO2

Amazon Daily Closing Stock Price

Monthly Milk Production

Yearly Earthquakes

Have you ever tried to predict the future? What lies ahead is a mystery which is usually only solved by waiting. In this course, you will stop waiting and learn to use the powerful ARIMA  class models to forecast the future. You will learn how to use the statsmodels package to analyze time series, to build tailored models, and to forecast under uncertainty. How will the stock market move in the next 24 hours? How will the levels of CO2 change in the next decade? How many earthquakes will there be next year? You will learn to solve all these problems and more.

Have you ever tried to predict the future? What lies ahead is a mystery that is usually only solved by waiting. In this course, you can stop waiting and dive into the world of time series modeling using ARIMA models in Python to forecast the future.<br><br><h2>Time series data</h2>
Start by learning the basics of time series data, including the concept of stationarity—crucial for working with ARMA models. You'll learn how to test for stationarity both visually and statistically, generate ARMA data, and fit ARMA models to get a solid foundation.​<br><br><h2>Statsmodels package</h2>
As you progress, explore the powerful Statsmodels package for fitting ARMA, ARIMA, and ARMAX models. You'll get hands-on experience using your models to predict future values like stock prices.<br><br>
Making these concepts easy to grasp and apply, you’ll uncover generating one-step-ahead predictions, dynamic forecasts, and fitting ARIMA models directly to your data.<br><br><h2>ACF and PACF plots</h2>
One of the highlights is learning how to choose the best model using ACF and PACF plots to identify promising model orders. You'll learn about criteria like AIC and BIC for model selection and diagnostics, helping you refine your models to perfection​​.<br><br><h2>SARIMA models</h2>
The course wraps up with seasonal ARIMA (SARIMA) models, perfect for handling data with seasonal patterns. You'll learn to decompose time series data into seasonal and non-seasonal components and apply your ARIMA skills in a global forecast challenge. 
<br><br>This final project ties everything together, giving you a comprehensive understanding of ARIMA modeling.

Supervised Learning with scikit-learn

Learn about ARIMA models in Python and become an expert in time series analysis.

ARIMA Models in Python

Series temporales en Python

Climate informatics researcher

Overview

The augmented Dicky-Fuller test

Applying the adfuller test

Interpreting the test result

The value of plotting

Making a time series stationary

Other transforms

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_17329/subtitles/course_17329_6d32d1485b714c0b1590d2fd3a68b9a3.vtt

ARMA Models - Making time series stationary

Making time series stationary

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