Here are 16 interview questions related to Prophet, a forecasting library developed by Facebook, along with their answers:
1. What is Prophet?
Ans: Prophet is an open-source forecasting library developed by Facebook. It is designed to make accurate time series forecasts with minimal configuration.
2. What are the key features of the Prophet?
Ans: The key features of Prophet include automatic seasonality detection, handling of missing data, robustness to outliers, and the ability to incorporate user-specified inputs and events.
3. What types of time series forecasting problems can Prophet handle?
Ans: Prophet can handle a wide range of time series forecasting problems, including trend forecasting, seasonal forecasting, and forecasting with multiple seasonalities.
4. How does Prophet handle seasonality in time series data?
Ans: Prophet automatically detects and models various types of seasonality in the data, such as yearly, weekly, and daily patterns. It allows for flexible modeling of seasonality based on the data characteristics.
5. Can Prophet handle missing data in time series?
Ans: Yes, Prophet has built-in support for handling missing data in time series. It uses an interpolation method to impute missing values based on the observed trend and seasonality.
6. What are some advantages of using Prophet for time series forecasting?
Ans: Advantages of using Prophet include its ease of use, ability to handle complex seasonality, robustness to outliers, support for handling missing data, and the availability of uncertainty estimation.
7. Does Prophet provide uncertainty estimation for forecasts?
Ans: Yes, Prophet provides uncertainty estimation by generating prediction intervals around the forecasted values. These intervals capture the uncertainty and variability in the forecasts.
8. Can Prophet handle time series with external regressors or inputs?
Ans: Yes, Prophet allows for the inclusion of user-specified regressors or inputs that can help improve the accuracy of forecasts. These external factors can be related to the time series being forecasted.
9. How does Prophet handle outliers in time series data?
Ans: Prophet uses a robust modeling approach that is less sensitive to outliers. It automatically detects and down-weights outliers during the trend estimation process.
10. Does Prophet support forecasting at different time granularities?
Ans: Yes, Prophet can handle time series forecasting at different granularities, such as hourly, daily, weekly, monthly, or yearly frequencies.
11. Can Prophet handle time series with multiple seasonalities?
Ans: Yes, Prophet supports modeling time series with multiple seasonal patterns, such as daily and yearly seasonality occurring simultaneously.
12. How can you evaluate the performance of Prophet forecasts?
Ans: Prophet provides built-in methods to evaluate the performance of forecasts, such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE).
13. Can Prophet capture trend changes or regime shifts in time series data?
Ans: Yes, Prophet can capture trend changes or regime shifts by using changepoint detection algorithms. It automatically detects and models shifts in the underlying trend.
14. Can Prophet handle time series data with non-uniform sampling intervals?
Ans: No, Prophet assumes that the time series data has uniform sampling intervals. If the data has irregular or non-uniform intervals, it needs to be preprocessed or interpolated before using Prophet.
15. How can you incorporate holidays or special events in Prophet?
Ans: Prophet provides a way to include custom holiday or event information in the forecasting model. You can define a list of holidays or events with their respective dates and provide it as input to the model.
16. Can Prophet handle time series with long-term trends?
Ans: Yes, Prophet can handle time series with long-term trends by automatically capturing and modeling the trend component using a piecewise linear or logistic function.