Compare model assumptions and features to pick the best forecaster for your series. Know when automatic seasonality and changepoints help versus when classic SARIMA shines.
Prophet is convenient when a series has multiple seasonalities and ______ built in.
only weekly seasonality
strictly stationary residuals
exogenous inputs only
holiday effects
Before fitting an ARIMA model to a non‑stationary series, a common step is ______.
Winsorizing outliers
one‑hot encoding
differencing the series
min‑max scaling
Prophet handles structural breaks in trend using automatic ______ detection.
unit‑root
rank
cointegration
changepoint
In SARIMA, the seasonal period parameter m is the ______.
maximum AR order
number of exogenous features
length of the seasonal cycle
forecast horizon
To include promotions or price as external drivers in ARIMA you would use ______.
pure ARMA without d
Prophet’s holiday component only
Holt‑Winters smoothing
ARIMAX/SARIMAX with exogenous variables
In Prophet, adding a price index or marketing spend is done via ______.
add_changepoint
Box‑Cox lambda
auto_arima
add_regressor
For time‑series model validation, the preferred method is ______.
LOOCV without ordering
random k‑fold across all rows
rolling (time‑based) cross‑validation
shuffle‑split CV
Prophet expects timestamped rows on a regular grid; irregular gaps should be ______ first.
randomly shuffled
sorted by target value
resampled or filled to a uniform frequency
scaled to zero mean
ARIMA residual diagnostics should resemble ______ if the model fits well.
white noise
periodic oscillations
a linear trend
heteroscedastic bursts only
Compared with ARIMA, Prophet’s uncertainty intervals also reflect ______ uncertainty by default.
target scaling
calendar week numbers
trend changepoint
feature selection
Starter
Good start—review core assumptions and diagnostics for each forecaster.
Solid
Nice work—fine‑tune changepoints, seasonality, and CV strategy.
Expert!
Excellent—confidently choose between SARIMA and Prophet for any series.
