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.
(20 reviews)
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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
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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
Instructor
Offered by
Recommended if you're interested in Business Essentials
University of Illinois Urbana-Champaign
University of Colorado Boulder
Fundação Instituto de Administração
University of Pennsylvania
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Reviewed on Oct 16, 2024
Best explanation of the key concepts in short time. Well done.
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
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