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Deployed on AWS
This algorithm detects anomaly points for continuous univariate time series that peaks up suddenly, using unsupervised machine learning.
Overview
This algorithm is a technique for detecting unusual patterns that differ from common patterns in a data set, and considers individual data points as outliers when they differ significantly from surrounding data. Various machine learning algorithms can find outliers that match the algorithm's characteristics, and they learn to receive higher scores as the distance between the distribution of normal data and the distribution of abnormal data increases, and as the proportion of abnormal data decreases. The machine learning algorithm with the highest final score is selected, and the parameters optimized for it are determined.
Highlights
- Machine Learning, Point Anomaly Detection, Hyper Parameter Optimization
Details
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Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.00 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.00 |
ml.m5.xlarge Training Recommended | Algorithm training on the ml.m5.xlarge instance type | $0.00 |
ml.m4.4xlarge Inference (Real-Time) | Model inference on the ml.m4.4xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Inference (Real-Time) | Model inference on the ml.m5.4xlarge instance type, real-time mode | $0.00 |
ml.m5.12xlarge Inference (Real-Time) | Model inference on the ml.m5.12xlarge instance type, real-time mode | $0.00 |
ml.m4.16xlarge Inference (Real-Time) | Model inference on the ml.m4.16xlarge instance type, real-time mode | $0.00 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.00 |
ml.c4.4xlarge Inference (Real-Time) | Model inference on the ml.c4.4xlarge instance type, real-time mode | $0.00 |
ml.c5.9xlarge Inference (Real-Time) | Model inference on the ml.c5.9xlarge instance type, real-time mode | $0.00 |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Required input parameters must be entered to enable model learning to prevent malfunction.
Additional details
Inputs
- Summary
You need to input time series unstructured data in csv format. Specify the time and target columns in the hyperparameters. The data index must be at least 300. Below is the method for setting hyperparameters.
- time_column: indicates time column name (format: %Y-%m-%d T%H:%M:%S).
- x_column: indicates the column name for detecting.
- index_column: epresents a column name that means a separator to create different models for each group.
- Input MIME type
- text/csv
https://github.com/AI-Advisor-ML-Marketplace/point-anomaly-detection/blob/master/data/training/train.csv
https://github.com/AI-Advisor-ML-Marketplace/point-anomaly-detection/blob/master/data/training/train.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
value | If you do not change target_column and time_column with hyperparameters, the column name as shown below is recognized as default and the existence of the column is checked in the input data.
- value: target column (single column)
- timestamp: time format data (UTC format) | Type: Continuous | Yes |
timestamp | If you do not change target_column and time_column with hyperparameters, the column name as shown below is recognized as default and the existence of the column is checked in the input data.
- value: target column (single column)
- timestamp: time format data (UTC format) | Type: Continuous | Yes |
Resources
Vendor resources
Support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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