Data Science Time Series Analysis 2

Question 1

In ARIMA(p, d, q), what does the parameter 'd' represent?

Accepted Answer

The number of times the data is differenced to achieve stationarity

Answer

The degree of autocorrelation in the series

Answer

The number of MA (moving average) terms

Answer

The number of AR (autoregressive) terms

Question 2

The Augmented Dickey-Fuller (ADF) test is primarily used to:

Accepted Answer

Test whether a time series has a unit root and is non-stationary

Answer

Detect the presence of seasonality in a time series

Answer

Select the optimal number of ARIMA parameters automatically

Answer

Measure the out-of-sample forecasting accuracy of a model

Question 3

In simple exponential smoothing, the smoothing parameter α controls:

Accepted Answer

The rate at which the influence of older observations decays

Answer

The total number of observations included in each forecast

Answer

The weight applied to the seasonal component only

Answer

The strength of the trend component in the forecast

Question 4

What is the primary purpose of time series decomposition?

Accepted Answer

To separate the series into trend, seasonality, and residual components

Answer

To remove all randomness and noise from the data entirely

Answer

To convert a non-stationary series into a stationary one for modeling

Answer

To automatically select optimal ARIMA model parameters

Question 5

A simple moving average of order k is computed as:

Accepted Answer

The arithmetic mean of the k most recent observations

Answer

The exponentially weighted sum of all past observations

Answer

The median of the last k observations in the series

Answer

The weighted sum of lagged values defined by an AR model

Question 6

The Holt-Winters (Triple Exponential Smoothing) method is designed to handle:

Accepted Answer

Both trend and seasonality simultaneously in a time series

Answer

Only trend but not seasonality in a time series

Answer

Only seasonality but not trend in the data

Answer

Strictly stationary time series with no trend or seasonality

Question 7

Which forecasting accuracy metric is most appropriate when you want a scale-independent measure suitable for comparing across datasets with different magnitudes?

Accepted Answer

Mean Absolute Percentage Error (MAPE)

Answer

Mean Absolute Error (MAE)

Answer

Root Mean Square Error (RMSE)

Answer

Sum of Squared Errors (SSE)