What is the primary purpose of "lagging" in time series analysis?
A. To identify seasonality in the data
B. To remove outliers from the data
C. To add noise to the data
D. To identify trends in the data
In time series analysis, what is the "autoregressive" (AR) order in an ARIMA model?
A. The order of differencing
B. The order of the autoregressive component
C. The order of moving average
D. The order of seasonality
What is the primary goal of "seasonal differencing" in time series analysis?
A. To identify seasonality in the data
B. To remove seasonality from the data
C. To make the data stationary
D. To identify trends in the data
In the context of time series analysis, what is the "Box-Cox transformation" used for?
A. To identify seasonality in the data
B. To remove outliers from the data
C. To transform non-normal data
D. To add noise to the data
Which statistical test is commonly used to test for the presence of seasonality in a time series data set?
A. Augmented Dickey-Fuller test
B. Seasonal Decomposition of Time Series
C. Ljung-Box test
D. t-test
What is the primary goal of "differencing" in time series analysis?
A. To remove seasonality from the data
B. To remove outliers from the data
C. To identify trends in the data
D. To make the data stationary
Which time series forecasting method involves using a weighted average of past observations with exponentially decreasing weights?
A. Moving Average
B. Exponential Smoothing
C. ARIMA
D. Seasonal Decomposition of Time Series
Which statistical method is commonly used to test the stationarity of a time series data set?
A. Augmented Dickey-Fuller test
B. Chi-squared test
C. Mann-Whitney U test
D. t-test
What is the primary objective of "forecasting" in time series analysis?
A. To analyze past data trends
B. To predict future values
C. To identify seasonality in the data
D. To add noise to the data
In time series analysis, what is the purpose of the "seasonal differencing" step in the Box-Jenkins methodology (ARIMA modeling)?
A. To remove outliers from the data
B. To remove seasonality from the data
C. To identify trends in the data
D. To add noise to the data
