| ... | ... | @@ -14,7 +14,7 @@ The three web services allow the individual and remote invocation of the forecas |
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| Parameter | Description | Required | Valid Inputs |
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| horizon | The forecasting horizon up to which forecasts will be produced. | Yes | An integer in range [1-N], where N depends on the volume of data used to train the regressor. Currently there is no upper limit and the service returns an error if this value is set too high. |
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| project | The project ID for which the forecasts will be produced. | Yes | Currently the following string values are supported for testing purposes:<br>· TD Forecaster: ‘apache_kafka_measures’<br>· Security Forecaster: ‘square_retrofit_security_measures’<br>· Energy Forecaster: ‘sbeamer_gapbs_energy_measures’<br>Later, this input will be the ID of an actual project integrated into the SDK4ED platform. |
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| project | The project ID for which the forecasts will be produced. | Yes | Currently the following string values are supported for testing purposes:<br>· TD Forecaster: ‘apache_kafka’<br>· Security Forecaster: ‘square_retrofit’<br>· Energy Forecaster: ‘sbeamer_gapbs’<br>Later, this input will be the ID of an actual project integrated into the SDK4ED platform. |
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| regressor | The regressor model that will be used to produce forecasts. | No | One of the following string values: [‘auto’, ‘mlr’, ‘lasso’, ‘ridge’, ‘svr_linear’, ‘svr_rbf’, ‘random_forest’, ‘arima’].<br>Default value is ‘auto’. If this parameter is omitted, default value is set to ‘auto’ and the service selects automatically the best model based on validation error minimization. |
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| ground_truth | If the model will return also ground truth values or not. | No | One of the following string values: [‘yes’, ‘no’].<br>Default value is ‘no. |
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| test | If the model will produce Train-Test or unseen forecasts. | No | One of the following string values: [‘yes’, ‘no’].<br>Default value is ‘no’. If set to ‘no’, then the service uses the whole data to train a regressor and returns forecasts on unseen data. A value of ‘yes’ should be used only for model testing and not actual deployment into production. |
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