Whitepaper
Outlier Detection for Real-World Cases
The development of data-intensive and machine learning-based applications is always associated with processing a lot of data, which creates the problem of dealing with abnormalities. Such events might lead to potential errors and undesirable consequences, as well as negative experiences from applying such complex systems, making it increasingly important to deal with them for industry.
Outlier detection is a fundamental issue that concerns events of abnormal behavior that might come up in different business scenarios. There exist specific outlier detection systems that should be properly designed and adopted for real-world cases.
In this whitepaper, we will:
- Investigate different types of outlier detection algorithms
- Provide the most promising methods for detecting outliers
- Compare candidate models through different indicators
- Conclude which models are more appropriate for real-world problems
Potential anomalousness in data can lead to amendable consequences. Despite the existence of many methods to deal with them, there are no all-inclusive approaches, which makes it challenging to determine the most appropriate. We have conducted research on the topic and are ready to share it with you.
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The development of data-intensive and machine learning-based applications is always associated with processing a lot of data, which creates the problem of dealing with abnormalities. Such events might lead to potential errors and undesirable consequences, as well as negative experiences from applying such complex systems, making it increasingly important to deal with them for industry.
Outlier detection is a fundamental issue that concerns events of abnormal behavior that might come up in different business scenarios. There exist specific outlier detection systems that should be properly designed and adopted for real-world cases.
In this whitepaper, we will:
- Investigate different types of outlier detection algorithms
- Provide the most promising methods for detecting outliers
- Compare candidate models through different indicators
- Conclude which models are more appropriate for real-world problems
Potential anomalousness in data can lead to amendable consequences. Despite the existence of many methods to deal with them, there are no all-inclusive approaches, which makes it challenging to determine the most appropriate. We have conducted research on the topic and are ready to share it with you.
