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
Which Python library provides ARIMA and SARIMA models for time series forecasting?
Answer: statsmodels
The `statsmodels` library provides ARIMA, SARIMA, and other classical time series modeling tools in Python.
What does 'stationarity' mean in the context of time series analysis?
Answer: Statistical properties like mean and variance are constant over time
A stationary time series has a constant mean, variance, and autocorrelation structure that do not change over time.
Which test is used to check for stationarity in a time series?
Answer: Augmented Dickey-Fuller test
The Augmented Dickey-Fuller (ADF) test checks for a unit root; a low p-value indicates the series is stationary.
How do you parse a date column automatically when reading a CSV file with pandas?
Answer: pd.read_csv(file, parse_dates=['date'])
The `parse_dates` parameter in `pd.read_csv()` automatically converts specified columns to pandas datetime objects.
What does an ACF (Autocorrelation Function) plot show in time series analysis?
Answer: The correlation of the series with its own lagged values
ACF plots display the correlation between a time series and its lagged versions, helping identify the MA (q) order for ARIMA.
In pandas, how do you resample a time series to monthly frequency and compute the mean?
Answer: df.resample('M').mean()
`df.resample('M').mean()` resamples a DatetimeIndex time series to monthly buckets and computes each month's mean.
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