Data Science with Python Certification Time Series Analysis and Forecasting 1

Question 1

Which Python library provides ARIMA and SARIMA models for time series forecasting?

Accepted Answer

statsmodels

Answer

The `statsmodels` library provides ARIMA, SARIMA, and other classical time series modeling tools in Python.

Question 2

What does 'stationarity' mean in the context of time series analysis?

Accepted Answer

Statistical properties like mean and variance are constant over time

Answer

A stationary time series has a constant mean, variance, and autocorrelation structure that do not change over time.

Question 3

Which test is used to check for stationarity in a time series?

Accepted Answer

Augmented Dickey-Fuller test

Answer

The Augmented Dickey-Fuller (ADF) test checks for a unit root; a low p-value indicates the series is stationary.

Question 4

How do you parse a date column automatically when reading a CSV file with pandas?

Accepted Answer

pd.read_csv(file, parse_dates=['date'])

Answer

The `parse_dates` parameter in `pd.read_csv()` automatically converts specified columns to pandas datetime objects.

Question 5

What does an ACF (Autocorrelation Function) plot show in time series analysis?

Accepted Answer

The correlation of the series with its own lagged values

Answer

ACF plots display the correlation between a time series and its lagged versions, helping identify the MA (q) order for ARIMA.

Data Science with Python Certification Practice Test