Time Series Mastery: Forecasting with ETS, ARIMA, Python

This course is part of multiple programs.

Instructors: Diogo Resende +1 more

2,987 already enrolled

Included with

1 module

Gain insight into a topic and learn the fundamentals.

45 reviews

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

45 reviews

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Apply the most widely used techniques, including Exponential Smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA).
Analyze real-world data to identify patterns and make accurate predictions.
Create advanced forecasting models using Python.

Skills you'll gain

Category: Predictive Modeling
Category: Model Evaluation
Category: Business Analytics
Category: Time Series Analysis and Forecasting
Category: Trend Analysis
Category: Data-Driven Decision-Making
Category: Statistical Analysis
Category: Strategic Decision-Making
Category: Advanced Analytics
Category: Predictive Analytics
Category: Forecasting

Details to know

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Assessments

4 assignments

Taught in English

91%

of learners achieved a positive career outcome

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There is 1 module in this course

In today's data-driven world, the ability to accurately forecast and predict future trends is crucial for businesses to stay ahead of the competition. Time series analysis is a powerful tool that allows organizations to unravel patterns and make informed decisions. This course, Time Series Mastery: Unravelling Patterns with ETS, ARIMA, and Advanced Forecasting Techniques, provides a comprehensive introduction to time series analysis and forecasting. You will learn about the most widely used techniques, including Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA), and advanced forecasting methods. By the end of this course, you will have the skills and knowledge to apply these techniques to real-world data and make accurate predictions.

What's included

16 videos4 readings4 assignments

16 videosTotal 71 minutes

Introduction to the Course: The Adventure Begins1 minute
What is Time Series Data?4 minutes
Python: Becoming One with the Data 6 minutes
ETS and Seasonal Decomposition 4 minutes
Python: ETS and Seasonal Decomposition 6 minutes
Evaluating Time Series Forecasting Models 5 minutes
Python: Training & Testing 4 minutes
What is ARIMA? 4 minutes
Python: ARIMA 4 minutes
Python: Evaluating Time Series Models 1 minute
SARIMA 4 minutes
Python: SARIMA 6 minutes
SARIMAX 4 minutes
Python: SARIMAX 7 minutes
Python: Predicting the Future 9 minutes
Congratulations! What's Next?1 minute

4 readingsTotal 20 minutes

Welcome to the Course: Course Overview5 minutes
How Uber Uses Time Series Analysis5 minutes
Predictive Analytics with Time-Series Machine Learning on Amazon Timestream 5 minutes
Centralized Forecasting Service: Revolutionizing Time Series Models at Sam’s Club 5 minutes

4 assignmentsTotal 50 minutes

Knowledge Check: Introduction to Time Series Analysis and ETS 10 minutes
Knowledge Check: ARIMA10 minutes
Knowledge Check: Advanced Forecasting with SARIMA and SARIMAX 10 minutes
Final Assessment20 minutes

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Instructors

Instructor ratings

(9 ratings)

Diogo Resende

Coursera

3 Courses9,124 learners

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Reviewed on Jun 12, 2024

I think it was too basic, it lacks more a deeper dive into theoretical aspects and importance about the different scores that the summary of the model provides. However it's a good introduction

Reviewed on Oct 16, 2024

Best explanation of the key concepts in short time. Well done.

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