incline.trending¶

incline.trending(estimates, k=5, how='mean', weighting='uniform', confidence_level=0.95, ids=None)[source]¶

Rank series by recent trend strength.

Parameters:

estimates (Mapping[str, TrendEstimate] | Sequence[TrendEstimate]) – Estimates to rank, either as a mapping from identifier to estimate or as a sequence alongside ids.
k (int) – How many of the most recent observations to summarize.
how (Aggregation) – 'mean', 'max', 'median' or 'last'. Only 'mean' and 'last' propagate uncertainty exactly.
weighting (Weighting) – Weighting across the window for 'mean'.
confidence_level (float) – Confidence level for the reported interval.
ids (Sequence[str] | None) – Identifiers, when estimates is a sequence.

Returns:

One row per series with columns id, trend, trend_se, ci_lower, ci_upper, significant, se_exact, rank, sorted strongest first. The schema does not change when the input is empty.

Raises:

ValueError – If identifiers cannot be determined.

Return type:

pd.DataFrame