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.
Time Series Mastery: Forecasting with ETS, ARIMA, Python
Instructors: Diogo Resende
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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 prompts3 plugins
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Reviewed on Oct 17, 2024
Best explanation of the key concepts in short time. Well done.
Reviewed on Jun 13, 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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