Time Series Analysis and Forecasting Flashcards
6 cards from real Data Science with Python Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Time Series Analysis and Forecasting flashcards as text
What does the 'd' parameter represent in the ARIMA(p, d, q) model?
Answer: The degree of differencing required to make the series stationary
The 'd' parameter specifies how many times the series must be differenced to achieve stationarity before modeling.
What is the purpose of seasonal decomposition in time series analysis?
Answer: To separate a series into trend, seasonal, and residual components
Seasonal decomposition splits a time series into trend, seasonal, and irregular residual components for separate analysis.
Which pandas method creates a rolling window calculation on a time series?
Answer: df.rolling()
`df.rolling(window)` creates a rolling window object enabling moving averages and other window-based aggregations.
What does the PACF (Partial Autocorrelation Function) help determine in ARIMA modeling?
Answer: The AR (p) order
PACF shows the direct correlation between a series and each lag after removing intermediate lag effects, identifying the AR (p) order.
How do you shift a pandas time series forward by one period to create a lagged feature?
Answer: df.shift(1)
`df.shift(1)` moves all values forward by one period, inserting NaN at the start — a standard way to create lag features.
Which forecasting model captures both trend and seasonality using exponential smoothing?
Answer: Holt-Winters model
The Holt-Winters (triple exponential smoothing) model accounts for level, trend, and seasonal components simultaneously.
How this page was made: written with AI tools and published by Nathan Beja, founder of Practice Test Geeks, who is responsible for its content. We have not yet matched this exam's facts to an official source, so confirm fees, dates and rules with the exam body. Not reviewed by a subject-matter expert. Editorial policy