Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet: Multiplicative Seasonality, Outliers,

The video tutorial covers the use of the Profit library for time series forecasting. It begins with setting up the environment and preparing the data. The initial model is built with additive seasonality, and its limitations are discussed. The tutorial then explores multiplicative seasonality and log transformation to improve model accuracy. Finally, it addresses handling outliers by removing them to enhance prediction confidence.

What is the primary focus of the new notebook introduced in the video?

• Investigating logistic growth models

• Studying linear regression models

• Analyzing daily data with additive seasonality

• Exploring multiplicative seasonality, outliers, and non-daily time intervals

Why is the initial model unable to accurately predict the seasonal component?

• Because it uses multiplicative seasonality

• Due to the presence of outliers

• Because it uses additive seasonality

• Due to incorrect data formatting

What improvement does the second model with multiplicative seasonality show?

• Reduced data processing time

• Increased prediction intervals

• Improved matching of peaks and troughs

• Better handling of outliers

What transformation is applied to the data in the third model to handle seasonality?

• Exponential transformation

• Log transformation

• Square root transformation

• Linear transformation

How does the log transformation affect the model's sensitivity to change points?

• It increases the model's sensitivity

• It makes the model less sensitive

• It has no effect on sensitivity

• It eliminates change points

What is the recommended method for dealing with outliers in Prophet?

• Applying a log transformation

• Removing the outliers

• Using a different dataset

• Adjusting the model parameters

What effect do outliers have on the prediction intervals of the model?

• They make the intervals smaller

• They have no effect

• They eliminate the intervals

• They make the intervals larger

What is the impact of removing outliers on the model's prediction intervals?

• The intervals become larger

• The intervals become smaller

• The intervals remain unchanged

• The intervals are eliminated

What is the purpose of creating fake outliers in the dataset?

• To test the model's robustness

• To improve data accuracy

• To reduce prediction intervals

• To eliminate seasonality

What is the main challenge when dealing with outliers in time series data?

• They reduce the model's accuracy

• They eliminate seasonality

• They make the model less confident in predictions

• They increase data processing time