History and forecast utilizing advanced machine learning models. Please be mindful of enhanced execution times (~1-2s per timeseries).
643 matches
62 actions
Growth Services
62 actions
- ABC AnalysisPOSTv1.0.0
- ABCxyz AnalysisPOSTv1.0.0
- ABCxyz AnalysisPOSTv1.0.0
- Bottom up forecastingPOSTv1.0.0
- Bundle pricingPOSTv1.0.0
- Calculate Amazon Inventory Performance Index (IPI)POSTv1.0.0
- Calculate economic order quantityPOSTv1.0.0
- Calculate fill ratePOSTv1.0.0
- Calculate financial impact of forecast accuracyPOSTv1.0.0
- Calculate inventory turnsPOSTv1.0.0
- Calculate lead time demandPOSTv1.0.0
- Calculate minimum order quantityPOSTv1.0.0
- Calculate optimal service levelPOSTv1.0.0
- Calculate service levelPOSTv1.0.0
- Carrying CostPOSTv1.0.0
- Create organizationPOSTv1.0.0
- Create userPOSTv1.0.0
- Delete organizationDELETEv1.0.0
- Delete planning levelDELETEv1.0.0
- Delete userDELETEv1.0.0
- Forecast from filePOSTv1.0.0
- Forecast resultGETv1.0.0
- Forecast statusGETv1.0.0
- Forecast utilizing advanced machine learning modelsPOSTv1.0.0
- Forecasts only, for faster resultsPOSTv1.0.0
- Full forecast result details, including error, trend seasonality and outlierPOSTv1.0.0
- Get all common ModelsGETv1.0.0
- Get all organizationsGETv1.0.0
- Get all usersGETv1.0.0
- Get hyperparametersGETv1.0.0
- Get list of plannign levels by organizationGETv1.0.0
- Get list of plannign levels by userGETv1.0.0
- Get Models for OrganizationGETv1.0.0
- Get optimal parameter per methodPOSTv1.0.0
- Get outlierPOSTv1.0.0
- Get usage statistics per userGETv1.0.0
- History and forecast for fast timeseries viewPOSTv1.0.0
- History and forecast utilizing advanced machine learning modelsPOSTv1.0.0
- Inventroy Turn-overPOSTv1.0.0
- Lock planning levelPOSTv1.0.0
- Lock userPUTv1.0.0
- Map from old product to new product to create artifical historyPOSTv1.0.0
- Map from old product to new product to create artifical historyPOSTv1.0.0
- Month over month performance per planning levelGETv1.0.0
- Pause organizationPUTv1.0.0
- Planning level rewind to calculate and measure performance potential (internal versus iCUE).POSTv1.0.0
- post_pricing_competitive_pricingPOSTv1.0.0
- post_pricing_cost_plus_pricingPOSTv1.0.0
- post_pricing_decoy_pricingPOSTv1.0.0
- post_pricing_odd_pricingPOSTv1.0.0
- post_pricing_penetration_pricingPOSTv1.0.0
- post_pricing_price_elasticity_of_demandPOSTv1.0.0
- Re-order PointPOSTv1.0.0
- Register new forecasting modelPOSTv1.0.0
- Register new forecasting modelPOSTv1.0.0
- Safety StockPOSTv1.0.0
- Set hyperparametersPOSTv1.0.0
- SKU rationalization reportGETv1.0.0
- Top down forecastingPOSTv1.0.0
- Transform data file to JSON formatPOSTv1.0.0
- Update userPUTv1.0.0
- xyz AnalysisPOSTv1.0.0
Analytics/Growth Services
History and forecast utilizing advanced machine learning models
v1.0.0Input
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{
"type": "object",
"properties": {
"Token": {
"type": "string"
},
"data": {
"type": "array",
"items": {
"type": "object",
"properties": {
"historyValues": {
"type": "array",
"items": {
"type": "number",
"format": "double"
}
},
"timeSeriesId": {
"type": "string"
}
},
"additionalProperties": false
}
},
"method": {
"type": "string",
"enum": [
"icueMLP",
"icueMLO",
"icueMLN"
]
},
"params": {
"type": "object",
"required": [
"discardData",
"errorType",
"holdOutPeriod",
"noFcst",
"outlierDetection",
"periodicity"
],
"properties": {
"discardData": {
"type": "boolean"
},
"errorType": {
"type": "string",
"enum": [
"MeanAbsolutePercentageError",
"MeanSquaredError",
"MeanAbsoluteError",
"MedianAbsoluteDeviation",
"None"
]
},
"holdOutPeriod": {
"type": "integer",
"format": "int32"
},
"noFcst": {
"type": "integer",
"format": "int32"
},
"outlierDetection": {
"type": "boolean"
},
"periodicity": {
"type": "integer",
"format": "int32"
}
},
"additionalProperties": false
},
"planningLevelId": {
"type": "string"
},
"startDate": {
"type": "string"
}
},
"required": [
"method",
"planningLevelId",
"startDate"
],
"additionalProperties": false
}Output
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{
"type": "object",
"properties": {
"jobId": {
"type": "integer",
"format": "int32"
}
},
"additionalProperties": false
}