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ARIMA Models in Python

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4.7+
22 reviews
Updated 12/2024
Learn about ARIMA models in Python and become an expert in time series analysis.
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PythonMachine Learning4 hours15 videos57 exercises4,850 XP20,742Statement of Accomplishment

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Course Description

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.

Time series data

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.​

Statsmodels package

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.

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.

ACF and PACF plots

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​​.

SARIMA models

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.

This final project ties everything together, giving you a comprehensive understanding of ARIMA modeling.

Prerequisites

Supervised Learning with scikit-learn
1

ARMA Models

Start Chapter
2

Fitting the Future

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3

The Best of the Best Models

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4

Seasonal ARIMA Models

Start Chapter
ARIMA Models in Python
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*4.7
from 22 reviews
91%
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  • Flavio Z.
    3 months

    Nice course. Straight to the point. Always clear.

  • Saloua C.
    5 months

    I was looking for a course on classical models for time series forecasting that provides a thorough explanation of the theory and practical application on real data. This was the only course I found that met those criteria. It was very well-structured and explained. I especially appreciated the practical exercises that helped reinforce the material. Thank you very much! It would be great if more advanced models, such as those for handling double seasonality, could also be included.

  • Desislava B.
    11 months

    All important concepts very well explained, it helped me with new approches that I later used on my own data

  • Jordan B.
    about 1 year

    Good

  • Nadiia D.
    about 1 year

    During the whole track before I was not able to catch the main idea of AR, MA and ARIMA models. This course gives just brilliant explanations that are well structured. Thank you a lot for this one 🙏🏼

"Nice course. Straight to the point. Always clear."

Flavio Z.

"All important concepts very well explained, it helped me with new approches that I later used on my own data"

Desislava B.

"Good"

Jordan B.

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