Time Series Mastery: Forecasting with ETS, ARIMA, Python
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Time Series Mastery: Forecasting with ETS, ARIMA, Python
This course is part of multiple programs.
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.
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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.
Targeted at business analysts, data scientists, financial analysts, and market researchers, this course provides essential skills and insights to excel in today's data-driven business environment, equipping learners with the tools to drive strategic decision-making and foster organizational growth.
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 readings4 assignments
16 videos•Total 71 minutes
- Introduction to the Course: The Adventure Begins•1 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 readings•Total 20 minutes
- Welcome to the Course: Course Overview•5 minutes
- How Uber Uses Time Series Analysis•5 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 assignments•Total 50 minutes
- Final Assessment•20 minutes
- Knowledge Check: Introduction to Time Series Analysis and ETS •10 minutes
- Knowledge Check: ARIMA•10 minutes
- Knowledge Check: Advanced Forecasting with SARIMA and SARIMAX •10 minutes
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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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