Times Series Forecasting

3min

The below parameters are for various time series forecasting attack types. To know more about the different attack input parameters see Time Series Forecasting﻿. 
The values presented below are examples taken from the reference implementation. It is important to customize these values to achieve the best performance for your particular model.
Extraction
{
            "attack_type": "blackbox",
            "defense_bestonly": "no",
            "encryption_strategy": "0",
            "input_dimensions": "100,6",
            "model_api_details": "na",
            "model_framework": "tensorflow",
            "number_of_attack_queries": "10000",
            "use_model_api": "no",
            "vulnerability_threshold": "0"
}

{
            "attack_type": "blackbox",
            "defense_bestonly": "no",
            "encryption_strategy": "0",
            "input_dimensions": "100,6",
            "model_api_details": "na",
            "model_framework": "tensorflow",
            "number_of_attack_queries": "10000",
            "use_model_api": "no",
            "vulnerability_threshold": "0"
}
﻿
POST
Request
Path Params
model_id
required
String
Header Parameters
Org_id
required
String
Enter your organization id
x-api-key
required
String
Enter your API key
﻿
Note: In the response, copy job_id. This value is required to trigger the GET APIs. See <Get> Job Metadata﻿.

The "job_id" is used to retrieve model analysis metadata and check the processing status of the request. It is also used for generating reports for the analyzed model. Copy the dashboard link from the response and open it in a new tab. This allows users to monitor the job progress and download reports.
﻿
For using Nodejs sample code, install npm package request
Given ample code for python is tested for Python version 3.7
Given Sample code for nodejs and javascript is tested for Node V20LTS 
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Updated 05 Aug 2024

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