Data Science Time Series Analysis

Question 1

In time series analysis, a series is considered stationary if:

Accepted Answer

Its mean, variance, and autocovariance are constant over time

Answer

A stationary time series has constant statistical properties (mean, variance, and autocovariance) over time, which is required by most classical time series models.

Question 2

What does the Autocorrelation Function (ACF) measure in a time series?

Accepted Answer

The correlation between a time series and a lagged version of itself

Answer

The ACF measures the linear correlation between a time series and its own past values at various lags, helping identify repeating patterns and appropriate model parameters.

Question 3

What does the 'I' stand for in the ARIMA model?

Accepted Answer

Integrated

Answer

The 'I' in ARIMA stands for 'Integrated,' referring to the differencing of the series to achieve stationarity before fitting the AR and MA components.

Question 4

What is seasonality in a time series?

Accepted Answer

Regular, periodic patterns that repeat at fixed time intervals

Answer

Seasonality refers to regular, repeating fluctuations that occur at fixed intervals such as daily, weekly, or yearly cycles — driven by factors like weather or business calendars.

Question 5

The Partial Autocorrelation Function (PACF) is primarily used to determine:

Accepted Answer

The order of the Autoregressive (AR) component

Answer

PACF measures the direct correlation between a series and its lags after removing the effect of intermediate lags, which helps identify the appropriate AR order (p) in ARIMA.

Data Science Practice Test

Data Science Practice Test

Data Science Time Series Analysis