datasets-diagnosticsInspect pmdarima's built-in time-series datasets, validate target and seasonal inputs, estimate differencing, and run numeric or headless diagnostics.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this route before fitting or selecting a forecasting model when the target series, observation calendar, missing-value policy, differencing need, or serial-dependence evidence is not explicit. Read the three linked references before making a material decision:
Run the bundled deterministic checker
for a small local smoke test. It does not fetch data, fit an ARIMA model, or
create plots unless --plot is explicitly requested.
as_series option, Python
type, shape, length, dtype, index/date semantics, and missing count. A
one-dimensional array does not carry frequency metadata; choose m from
domain/calendar evidence, never from the sample count or a plot. For
load_msft(), inspect the seven-column DataFrame, parse/sort Date only
when needed, and select one numeric target. Do not pass the full DataFrame
to univariate helpers. Avoid load_gasoline() in offline work: it can use
a network URL/cache and its documented weekly period is fractional.check_endog. Use force_all_finite=True before stationarity or seasonal
tests, ACF/PACF, and decomposition unless the task has an explicit missing
data policy. Use check_exog only for a two-dimensional exogenous matrix;
route feature construction to preprocessing.m > 1, call bounded ndiffs for non-seasonal d and nsdiffs for
seasonal D. Record test names, alpha, bounds, warnings, and output
lengths from any explicit diff calls. Compare tests only with their
hypotheses and settings recorded. These are bounded recommendations, not
final ARIMA order selection or proof that a model will forecast well.decompose with exactly
type_="additive" or type_="multiplicative", an integer m > 1, at
least two complete cycles, and a scale compatible with the chosen type.
Inspect endpoint NaNs in trend and random; check reconstruction only on
the finite interior. Do not feed padded components to another diagnostic
without a missing-value policy.acf and pacf with
explicit conservative lags and methods. PACF has stricter lag limits than
ACF. Treat these as descriptive evidence, not automatic p/q order
selection.show=False, and close all figures. tsdisplay requires
lag_max < len(y). A missing Matplotlib installation blocks only the
requested visualization path, not numeric diagnostics.d/D recommendations, transformed lengths, and
diagnostic evidence to forecasting. Send target
transforms, Fourier, or date features to preprocessing.
Send leakage-safe holdouts and temporal cross-validation to
model-selection. Persistence and update
belong to the sibling persistence route. This sub-skill does not fit the
primary ARIMA model, choose final orders, compare folds, or persist models.loader/as_series, type/shape/length/dtype, index or date evidence,
missing count and policy, calendar evidence and m, ndiffs/nsdiffs tests and
bounds, d/D and differenced lengths, decomposition type and endpoint NaNs,
ACF/PACF methods/lags/key values, plotting/backend status, warnings/errors,
package/runtime version and import/build status, unresolved assumptions
The evidence anchor is pmdarima v2.1.1 commit
4c2dfccb28f64d2c00a5e10b59c1d1a3e16576a9. Runtime files resolve imports from
the installed, successfully built pmdarima distribution and do not require a
source checkout.