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Time Series Mastery: Forecasting with ETS, ARIMA, Python
Coursera Instructor Network

Time Series Mastery: Forecasting with ETS, ARIMA, Python

Taught in English


Gain insight into a topic and learn the fundamentals

Diogo Resende

Instructor: Diogo Resende

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.

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May 2024


1 assignment

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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 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.

What's included

16 videos4 readings1 assignment2 discussion prompts


Diogo Resende
Coursera Instructor Network
1 Course31 learners

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