Data Science Time Series Analysis 3

Question 1

How does SARIMA differ from a standard ARIMA model?

Accepted Answer

SARIMA adds seasonal AR, differencing, and MA terms to capture seasonal patterns

Answer

SARIMA extends ARIMA by adding seasonal autoregressive, integrated, and moving average terms (SARIMA(p,d,q)(P,D,Q)m) to model periodic seasonal patterns alongside non-seasonal structure.

Question 2

Why is standard k-fold cross-validation inappropriate for time series forecasting evaluation?

Accepted Answer

It violates temporal ordering by using future data to train models predicting the past

Answer

Standard k-fold randomly shuffles observations, causing data leakage where future values influence training — this artificially inflates performance and is invalid for time-dependent data.

Question 3

What does the Granger causality test assess in time series analysis?

Accepted Answer

Whether past values of one series are useful for forecasting another series

Answer

Granger causality tests whether including lagged values of series X improves forecast accuracy of series Y beyond using Y's own history alone, indicating predictive (not causal) relationships.

Question 4

Long Short-Term Memory (LSTM) networks are well-suited for time series forecasting because:

Accepted Answer

They learn long-range dependencies and model complex non-linear patterns

Answer

LSTMs use gating mechanisms (input, forget, output gates) to selectively retain or discard information across long sequences, capturing complex temporal dependencies that linear statistical models cannot represent.

Question 5

Cointegration between two non-stationary time series implies:

Accepted Answer

A stable long-run equilibrium relationship exists between the two series

Answer

Cointegration means that even though each series individually is non-stationary, a linear combination of them is stationary — indicating a persistent, stable long-run equilibrium relationship.

Data Science Practice Test

Data Science Practice Test

Data Science Time Series Analysis 3