Data Science with Python Certification Time Series Analysis and Forecasting 2

Question 1

What does the 'd' parameter represent in the ARIMA(p, d, q) model?

Accepted Answer

The degree of differencing required to make the series stationary

Answer

The 'd' parameter specifies how many times the series must be differenced to achieve stationarity before modeling.

Question 2

What is the purpose of seasonal decomposition in time series analysis?

Accepted Answer

To separate a series into trend, seasonal, and residual components

Answer

Seasonal decomposition splits a time series into trend, seasonal, and irregular residual components for separate analysis.

Question 3

Which pandas method creates a rolling window calculation on a time series?

Accepted Answer

df.rolling()

Answer

`df.rolling(window)` creates a rolling window object enabling moving averages and other window-based aggregations.

Question 4

What does the PACF (Partial Autocorrelation Function) help determine in ARIMA modeling?

Accepted Answer

The AR (p) order

Answer

PACF shows the direct correlation between a series and each lag after removing intermediate lag effects, identifying the AR (p) order.

Question 5

How do you shift a pandas time series forward by one period to create a lagged feature?

Accepted Answer

df.shift(1)

Answer

`df.shift(1)` moves all values forward by one period, inserting NaN at the start — a standard way to create lag features.

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