| ... | ... | @@ -11,12 +11,12 @@ As it can be seen. the entry point of the Forecasting Toolbox is a RESTful web s |
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The three web services allow the individual and remote invocation of the forecasting models developed for estimating the evolution of TD, Energy and Security. This is achieved through the dedicated API exposed by the RESTful web server, which allows the user to perform simple HTTP GET requests to the three web services. Several inputs need to be provided as URL-encoded parameters to these requests. These parameters are listed below:
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| Parameter | Description | Required | Valid Inputs |
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|:------------:|:---------------------------------------------------------------:|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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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’<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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| Parameter | Description | Required | Valid Inputs |
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|:------------:|:---------------------------------------------------------------:|:--------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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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 | A string value representing the ID of the selected project for which a forecast was requested. This ID is used to retrieve the TD, Energy and Dependability analysis metrics from the corresponding Toolboxes’ DBs, which will then be used for forecasting model execution. Depending on the specific web service, this ID is constructed as follows:<br>· TD Forecaster: ‘<user_name>:<project_name>’<br>· Security Forecaster: ‘<user_name>:<project_name>’<br>· Energy Forecaster: ‘<project_name>’<br>Both <user_name> and <project_name> values are project properties retrieved from the SDK4ED Dashboard session storage. |
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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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The output of the three individual web services provided by the Forecasting Toolbox, namely TD Forecaster, Energy Forecaster and Dependability Forecaster is a JSON file containing the predicted values for a particular quality attribute of the selected application (see [Usage](Forecaster Toolbox Usage)). This JSON actually contains i) a status code of the response, ii) a N-size array containing the forecasts, where N is equal to the ‘horizon’ parameter, iii) a recap on the given parameter values, and iv) a message informing the user if the request was fulfilled successfully or not. |
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