Description
Construct nonnegative synthetic demand with selected perfect, signed-additive or multiplicative forecast patterns, retain parameters and zero-clamping, and delegate MAE plus logistics WMAPE/bias. All-zero actual demand makes WMAPE explicitly undefined, not zero; outputs preserve existing floating arithmetic and are controlled fixtures rather than forecast-quality guarantees.
A logistics test dataset combines supplied operating ranges with explicit quantity and unit conventions. Constructed measurements and calculated indicators can exercise reports and calculators without claiming to represent an observed operation or a safe physical plan.
Construct nonnegative synthetic demand with selected perfect, signed-additive or multiplicative forecast patterns, retain parameters and zero-clamping, and delegate MAE plus logistics WMAPE/bias. All-zero actual demand makes WMAPE explicitly undefined, not zero; outputs preserve existing floating arithmetic and are controlled fixtures rather than forecast-quality guarantees.
When to use this tool
Use this generator to create reproducible inventory forecast error dataset cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.
How to use the tool and how it works
- Supply minimumActual, maximumActual, patterns, minimumDelta, maximumDelta, factors, count in the field shapes below. Set seed when you need an explicit replay.
- Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
- Read rows, meanAbsoluteError, wmape, wmapeUndefinedReason. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.
Draw actual demand and an exact, additive, or multiplicative forecast pattern. Clamp negative forecasts to zero, retain raw forecast and parameters, then calculate mean absolute error and WMAPE where aggregate actual demand is positive.1
Delegated operations used for this result: wmape forecast accuracy logistics; mean absolute error. Their calculation or parsing conventions apply to the corresponding result fields.2, 3
| Input field | Meaning and units | Accepted input |
|---|---|---|
| minimumActual | Minimum Actual | Required. Safe integer; minimum 0; maximum 9007199254740991. |
| maximumActual | Maximum Actual | Required. Safe integer; minimum 0; maximum 9007199254740991. |
| patterns | Patterns | Required. Array of Text; one of "additive", "multiplicative", "perfect".; minimum items 1; maximum items 3. |
| minimumDelta | Minimum Delta | Required. Safe integer; minimum -9007199254740991; maximum 9007199254740991. |
| maximumDelta | Maximum Delta | Required. Safe integer; minimum -9007199254740991; maximum 9007199254740991. |
| factors | Factors | Required. Array of Finite number; minimum 0.; minimum items 1; maximum items 32. |
| count | Requested count; the mechanics explain whether one draw expands into several returned records. | Required. Safe integer; minimum 1; maximum 128. |
| seed | Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed. | Optional. Safe integer. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| rows | Rows | Required. Array of Object; no unknown keys.; minimum items 1; maximum items 128. |
| rows[].index | Index | Required. Safe integer; minimum 1; maximum 128. |
| rows[].pattern | Pattern | Required. Text; one of "additive", "multiplicative", "perfect". |
| rows[].parameter | Parameter | Required. Finite number. or Null. |
| rows[].actualUnits | Actual Units | Required. Safe integer; minimum 0; maximum 9007199254740991. |
| rows[].rawForecast | Raw Forecast | Required. Finite number. |
| rows[].forecastUnits | Forecast Units | Required. Finite number; minimum 0. |
| rows[].clampedAtZero | Clamped At Zero | Required. Boolean. |
| meanAbsoluteError | Mean Absolute Error | Required. Finite number; minimum 0. |
| wmape | Wmape | Required. Object; no unknown keys. or Null. |
| wmapeUndefinedReason | Wmape Undefined Reason | Required. Text; exactly "zero-actual-demand". or Null. |
| randomness | Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source. | Required. Object; no unknown keys. |
| randomness.seed | Optional numeric replay seed; normalization and precedence are explained below. | Required. Safe integer. or Null. |
| randomness.source | Source | Required. Text; one of "context", "seed". |
An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.
{
"input": {
"minimumActual": 0,
"maximumActual": 10,
"patterns": [
"additive",
"multiplicative",
"perfect"
],
"minimumDelta": -4,
"maximumDelta": 4,
"factors": [
0.5,
1.5
],
"count": 5,
"seed": 42
},
"output": {
"rows": [
{
"index": 1,
"pattern": "multiplicative",
"parameter": 1.5,
"actualUnits": 6,
"rawForecast": 9,
"forecastUnits": 9,
"clampedAtZero": false
},
{
"index": 2,
"pattern": "additive",
"parameter": 0,
"actualUnits": 7,
"rawForecast": 7,
"forecastUnits": 7,
"clampedAtZero": false
},
{
"index": 3,
"pattern": "multiplicative",
"parameter": 1.5,
"actualUnits": 3,
"rawForecast": 4.5,
"forecastUnits": 4.5,
"clampedAtZero": false
},
{
"index": 4,
"pattern": "additive",
"parameter": 3,
"actualUnits": 5,
"rawForecast": 8,
"forecastUnits": 8,
"clampedAtZero": false
},
{
"index": 5,
"pattern": "additive",
"parameter": -3,
"actualUnits": 8,
"rawForecast": 5,
"forecastUnits": 5,
"clampedAtZero": false
}
],
"meanAbsoluteError": 2.1,
"wmape": {
"absoluteErrorTotal": 10.5,
"actualTotal": 29,
"biasPercent": 15.517241379310345,
"wmapePercent": 36.206896551724135
},
"wmapeUndefinedReason": null,
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- WMAPE is null with zero-actual-demand reason when all actuals are zero. Pattern labels describe generation, not fitted forecasts or evidence of future accuracy; multiplicative forecasts can be fractional.
- A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
- The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.
Alternative or Complementary methods
For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.
References
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