Intermittent Demand Models

Specialized models for sparse, lumpy demand patterns with many zeros.

Model	Description
CrostonClassic	Classic Croston method for sparse demand
CrostonOptimized	Croston with optimized smoothing parameters
CrostonSBA	Syntetos-Boylan Approximation (bias-corrected)
ADIDA	Aggregate-Disaggregate Intermittent Demand
IMAPA	Intermittent Multiple Aggregation Prediction
TSB	Teunter-Syntetos-Babai (trending intermittent)

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CrostonClassic

Classic Croston method that separately forecasts demand size and inter-arrival times.

Example

SELECT * FROM anofox_fcst_ts_forecast(
    'spare_parts',
    'date',
    'demand',
    'CrostonClassic',
    30,
    MAP{}
);

Best for: Sparse demand, many zeros, spare parts inventory.

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CrostonOptimized

Croston method with optimized smoothing parameters.

Example

SELECT * FROM anofox_fcst_ts_forecast(
    'spare_parts',
    'date',
    'demand',
    'CrostonOptimized',
    30,
    MAP{}
);

Best for: Better variance estimation than classic Croston.

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CrostonSBA

Syntetos-Boylan Approximation - bias-corrected Croston.

Example

SELECT * FROM anofox_fcst_ts_forecast(
    'low_frequency',
    'date',
    'demand',
    'CrostonSBA',
    30,
    MAP{}
);

Best for: Reduced bias, low-frequency demand.

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ADIDA

Aggregate-Disaggregate Intermittent Demand Approach.

Example

SELECT * FROM anofox_fcst_ts_forecast(
    'very_sparse',
    'date',
    'demand',
    'ADIDA',
    30,
    MAP{}
);

Best for: Very sparse demand, complex intermittent patterns.

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IMAPA

Intermittent Multiple Aggregation Prediction Algorithm.

Example

SELECT * FROM anofox_fcst_ts_forecast(
    'complex_intermittent',
    'date',
    'demand',
    'IMAPA',
    30,
    MAP{}
);

Best for: Very complex intermittency, multiple temporal patterns.

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TSB

Teunter-Syntetos-Babai method - handles trending intermittent demand.

Example

SELECT * FROM anofox_fcst_ts_forecast(
    'trending_sparse',
    'date',
    'demand',
    'TSB',
    30,
    MAP{}
);

Best for: Intermittent + trending, growing/declining sparse demand.

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Comparison

Model	Trend Support	Bias Correction	Complexity
CrostonClassic	No	No	Simple
CrostonOptimized	No	No	Simple
CrostonSBA	No	Yes	Simple
ADIDA	No	Moderate	Medium
IMAPA	No	Yes	Complex
TSB	Yes	Yes	Medium

Demand Classification

Before choosing a model, classify your demand pattern:

Pattern	Zero %	Variation	Recommended
Smooth	Low	Low	AutoETS
Erratic	Low	High	MFLES
Intermittent	High	Low	CrostonSBA
Lumpy	High	High	TSB or ADIDA

When to Use Intermittent Models

Scenario	Recommended Model
Spare parts inventory	CrostonClassic
Low-frequency demand	CrostonSBA
Very sparse (>80% zeros)	ADIDA
Complex intermittency	IMAPA
Growing/declining sparse	TSB
Quick baseline	CrostonClassic

Example: Spare Parts Forecasting

-- Classify demand first
WITH classified AS (
    SELECT
        sku_id,
        COUNT(*) as n_obs,
        SUM(CASE WHEN demand = 0 THEN 1 ELSE 0 END)::DOUBLE / COUNT(*) as zero_pct
    FROM spare_parts_demand
    GROUP BY sku_id
)
-- Use appropriate model based on zero percentage
SELECT
    sku_id,
    CASE
        WHEN zero_pct > 0.7 THEN 'CrostonSBA'
        WHEN zero_pct > 0.3 THEN 'CrostonClassic'
        ELSE 'AutoETS'
    END as recommended_model
FROM classified;