Time Series Analysis and Forecasting Flashcards
6 cards from real Data Science with Python Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Time Series Analysis and Forecasting flashcards as text
What is the primary purpose of differencing in time series preprocessing?
Answer: To remove trends and make the series stationary
Differencing computes consecutive changes between observations to remove trends and achieve stationarity required by ARIMA.
In pandas, how do you set a datetime column as the DataFrame index for time series operations?
Answer: df.set_index('date', inplace=True)
`df.set_index('date', inplace=True)` sets the datetime column as the index, enabling time series methods like resample.
Which error metric is most commonly used to evaluate time series forecasting accuracy?
Answer: Mean Absolute Error (MAE)
Mean Absolute Error (MAE) measures the average absolute difference between forecasted and actual values, widely used for time series.
What does `pd.date_range()` generate in pandas?
Answer: A fixed-frequency sequence of datetime values
`pd.date_range()` generates a fixed-frequency DatetimeIndex useful for creating or reindexing time series data.
Which method fills missing values in a time series by estimating between known points?
Answer: df.interpolate(method='linear')
`df.interpolate(method='linear')` fills missing values by fitting a straight line between surrounding known data points.
What is the key difference between additive and multiplicative decomposition models?
Answer: In additive models components sum together; in multiplicative models they multiply
In additive decomposition series = trend + seasonal + residual; in multiplicative decomposition series = trend × seasonal × residual.
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