Demand Classification (AID)

Automatic Identification of Demand patterns with anomaly detection.

Reference: Svetunkov, I. & Sroginis, A. (2025). Why do zeroes happen? A model-based approach for demand classification. arXiv:2504.05894

aid

Classifies demand time series as regular or intermittent, fits the best distribution, and detects anomalies.

ps.aid(
    y: Union[pl.Expr, str],
    intermittent_threshold: float = 0.3,  # Zero proportion threshold
) -> pl.Expr

Returns: See AID Output

Example:

# Classify demand per SKU
result = df.group_by("sku").agg(
    ps.aid("demand").alias("classification")
).unnest("classification")

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aid_anomalies

Returns per-observation anomaly flags. Use with .over() to add anomaly columns to the original DataFrame.

ps.aid_anomalies(
    y: Union[pl.Expr, str],
    intermittent_threshold: float = 0.3,
) -> pl.Expr

Returns: See AID Anomalies Output

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Anomaly Types

Type	Description
stockout	Unexpected zero in otherwise positive demand
new_product	Leading zeros pattern (product launch)
obsolete_product	Trailing zeros pattern (product phase-out)
high_outlier	Unusually high demand value
low_outlier	Unusually low demand value

---

Example: Per-Row Anomaly Detection

# Add per-row anomaly flags
result = df.with_columns(
    ps.aid_anomalies("demand").over("sku").alias("anomalies")
).unnest("anomalies")

# Filter to flagged observations
flagged = result.filter(pl.col("high_outlier") | pl.col("stockout"))

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Supported Distributions

Category	Distributions
Count	poisson, negative_binomial, geometric
Continuous	normal, gamma, lognormal, rectified_normal

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See Also

• ALM - Augmented Linear Model distributions