Deep learning for time series data /
"Arun Kejariwal (Independent) and Ira Cohen (Anodot) share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. The first step uses anomaly detection algorithms to discover anomalies in a time series in the training data. In the second, multip...
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| Corporate Author: | |
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| Format: | Electronic Conference Proceeding Video |
| Language: | English |
| Published: |
[Place of publication not identified] :
O'Reilly,
2019.
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| Subjects: | |
| Online Access: | CONNECT |
MARC
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| 245 | 1 | 0 | |a Deep learning for time series data / |c Arun Kejariwal, Ira Cohen. |
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| 500 | |a Title from title screen (viewed November 14, 2019). | ||
| 518 | |a Recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York. | ||
| 511 | 0 | |a Presenters, Arun Kejariwal, Ira Cohen. | |
| 520 | |a "Arun Kejariwal (Independent) and Ira Cohen (Anodot) share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. The first step uses anomaly detection algorithms to discover anomalies in a time series in the training data. In the second, multiple prediction models, including time series models and deep networks, are trained, enriching the training data with the information about the anomalies discovered in the first step. Anomaly detection for individual time series is a necessary but insufficient step due to the fact that anomaly detection over a set of live data streams may result in anomaly fatigue, thereby limiting effective decision making. One way to address the above is to carry out anomaly detection in a multidimensional space. However, this is typically very expensive computationally and hence not suitable for live data streams. Another approach is to carry out anomaly detection on individual data streams and then leverage correlation analysis to minimize false positives, which in turn helps in surfacing actionable insights faster. Anomaly detection for individual time series is a necessary but insufficient step due to the fact that anomaly detection over a set of live data streams may result in anomaly fatigue, thereby limiting effective decision making. One way to address the above is to carry out anomaly detection in a multidimensional space. However, this is typically very expensive computationally and hence not suitable for live data streams. Another approach is to carry out anomaly detection on individual data streams and then leverage correlation analysis to minimize false positives, which in turn helps in surfacing actionable insights faster."--Resource description page | ||
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