Coursera

Time Series Mastery: Forecasting with ETS, ARIMA, Python (Coursera)

Offered by Coursera Instructor Network,
Time Series Mastery: Forecasting with ETS, ARIMA, Python (Coursera)

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.

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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.

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.

Syllabus

Time Series Mastery: Forecasting with ETS, ARIMA, Python
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.

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