All URIs are relative to https://api.brainrex.com
| Method | HTTP request | Description |
|---|---|---|
| anomalyBatch | POST /anomaly/json/detect | Detects anomalies in historical data in batches. This endpoint uses your entire dataset as input |
List<Boolean> anomalyBatch(body)
Detects anomalies in historical data in batches. This endpoint uses your entire dataset as input
The Anomaly Detect endpoint ingests time series data of all types, then monitors and detects abnormalities historical time series data. <br><br> Our AI selects from several models, choosing the one that fits the given data best, and we give you the avality to customize the sensitivy of the model. Our model has been trained to recognize anomalies in popular blockchain networks e.g. Bitcoin, Ethereum, learning from past events.
// Import classes:
//import io.swagger.client.ApiClient;
//import io.swagger.client.ApiException;
//import io.swagger.client.Configuration;
//import io.swagger.client.auth.*;
//import io.swagger.client.api.AnomalyApi;
ApiClient defaultClient = Configuration.getDefaultApiClient();
// Configure API key authorization: APIKeyHeader
ApiKeyAuth APIKeyHeader = (ApiKeyAuth) defaultClient.getAuthentication("APIKeyHeader");
APIKeyHeader.setApiKey("YOUR API KEY");
// Uncomment the following line to set a prefix for the API key, e.g. "Token" (defaults to null)
//APIKeyHeader.setApiKeyPrefix("Token");
AnomalyApi apiInstance = new AnomalyApi();
List<PointTimeSeries> body = Arrays.asList(new PointTimeSeries()); // List<PointTimeSeries> | Time Series to be analyzed, with the following format.
try {
List<Boolean> result = apiInstance.anomalyBatch(body);
System.out.println(result);
} catch (ApiException e) {
System.err.println("Exception when calling AnomalyApi#anomalyBatch");
e.printStackTrace();
}| Name | Type | Description | Notes |
|---|---|---|---|
| body | List<PointTimeSeries> | Time Series to be analyzed, with the following format. | [optional] |
List<Boolean>
- Content-Type: application/json
- Accept: application/json