Data Science - Time Series Forecasting with Facebook Prophet in Python - Forecasting Metrics

The video tutorial covers various error metrics used in time series analysis, including the sum of squared errors, mean squared error, root mean squared error, and mean absolute error. It explains the advantages and disadvantages of each metric, particularly in terms of scale and interpretability. The tutorial also introduces scale invariant metrics like R-squared and percentage error metrics such as MAPE and SMAPE, highlighting their uses and limitations. The goal is to familiarize viewers with these metrics to enhance their understanding and application in real-world scenarios.

What is the sum of squared errors and how is it calculated?

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Why do we square the differences in the sum of squared errors?

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What is the mean squared error and how does it differ from the sum of squared errors?

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Explain the concept of root mean squared error (RMSE) and its advantages.

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What is the difference between squared error and absolute error in terms of their probabilistic interpretations?

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Why might a model trained using squared error report absolute error as a metric?

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What are the limitations of using mean squared error and mean absolute error?

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How does the R-squared metric provide a scale-invariant measure of model performance?

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What is the mean absolute percentage error (MAPE) and what is its downside?

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Discuss the significance of having multiple error metrics in time series analysis.

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