Time Series Analysis Flashcards
7 cards from real Data Science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Time Series Analysis flashcards as text
In time series analysis, a series is considered stationary if:
Answer: Its mean, variance, and autocovariance are constant over time
A stationary time series has constant statistical properties (mean, variance, and autocovariance) over time, which is required by most classical time series models.
What does the Autocorrelation Function (ACF) measure in a time series?
Answer: The correlation between a time series and a lagged version of itself
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.
What does the 'I' stand for in the ARIMA model?
Answer: Integrated
The 'I' in ARIMA stands for 'Integrated,' referring to the differencing of the series to achieve stationarity before fitting the AR and MA components.
What is seasonality in a time series?
Answer: Regular, periodic patterns that repeat at fixed time intervals
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.
The Partial Autocorrelation Function (PACF) is primarily used to determine:
Answer: The order of the Autoregressive (AR) component
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.
What is white noise in time series analysis?
Answer: A series of uncorrelated random variables with constant mean and variance
White noise is a sequence of uncorrelated random variables with zero mean and constant variance — it contains no predictable structure and represents the ideal model residuals.
What does 'trend' represent in a time series?
Answer: The long-term increase or decrease in the data over an extended period
Trend represents the long-term, systematic directional movement (upward or downward) in the data, distinct from seasonal cycles or random noise.