Demand Classification

Classify demand patterns and detect anomalies for inventory management.

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AID - Demand Classification

Classifies demand patterns as regular or intermittent, identifies best-fit distribution, and detects various anomaly patterns.

Parameters

Parameter	Type	Required	Default	Description
y	DOUBLE	Yes	-	Demand values over time
options	MAP	No	-	Configuration options

Options MAP:

Option	Type	Default	Description
intermittent_threshold	DOUBLE	0.3	Zero proportion cutoff for intermittent classification
outlier_method	VARCHAR	zscore	Outlier detection: zscore (mean±3σ) or iqr (1.5×IQR)

Output Fields

Field	Type	Description
demand_type	VARCHAR	regular or intermittent
is_intermittent	BOOLEAN	True if zero_proportion >= threshold
distribution	VARCHAR	Best-fit distribution name
mean	DOUBLE	Mean of values
variance	DOUBLE	Variance of values
zero_proportion	DOUBLE	Proportion of zero values
n_observations	BIGINT	Number of observations
has_stockouts	BOOLEAN	True if stockouts detected
is_new_product	BOOLEAN	True if new product pattern (leading zeros)
is_obsolete_product	BOOLEAN	True if obsolete pattern (trailing zeros)
stockout_count	BIGINT	Number of stockout observations
new_product_count	BIGINT	Number of leading zero observations
obsolete_product_count	BIGINT	Number of trailing zero observations
high_outlier_count	BIGINT	Number of unusually high values
low_outlier_count	BIGINT	Number of unusually low values

Example

SELECT
    sku_id,
    (result).demand_type,
    (result).is_intermittent,
    (result).distribution,
    (result).zero_proportion,
    (result).has_stockouts,
    (result).high_outlier_count
FROM (
    SELECT
        sku_id,
        anofox_stats_aid_agg(
            daily_demand,
            MAP {'intermittent_threshold': '0.3', 'outlier_method': 'zscore'}
        ) as result
    FROM inventory_data
    GROUP BY sku_id
);

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AID Anomaly Detection

Returns per-observation anomaly flags for demand analysis while maintaining input order.

Parameters

Parameter	Type	Required	Default	Description
y	DOUBLE	Yes	-	Demand values
options	MAP	No	-	Configuration options

Options MAP:

Option	Type	Default	Description
intermittent_threshold	DOUBLE	0.3	Zero proportion cutoff
outlier_method	VARCHAR	zscore	Outlier detection: zscore or iqr

Output Fields (per observation)

Returns LIST(STRUCT) with one entry per input observation:

Field	Type	Description
stockout	BOOLEAN	Unexpected zero in positive demand
new_product	BOOLEAN	Leading zeros pattern
obsolete_product	BOOLEAN	Trailing zeros pattern
high_outlier	BOOLEAN	Unusually high value
low_outlier	BOOLEAN	Unusually low value

Example

SELECT anofox_stats_aid_anomaly_agg(
    daily_demand,
    MAP {'outlier_method': 'iqr'}
) as anomalies
FROM demand_series;

Anomaly Definitions

Anomaly	Definition
Stockout	Zero value occurring between non-zero values (not at start or end)
New Product	Leading sequence of zeros (before first non-zero)
Obsolete Product	Trailing sequence of zeros (after last non-zero)
High Outlier	Value > mean + 3×std (zscore) or > Q3 + 1.5×IQR (iqr)
Low Outlier	Non-zero value < mean - 3×std (zscore) or < Q1 - 1.5×IQR (iqr)