Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Anomaly detection in a mobile data network
The dissertation investigated the creation of an anomaly detection approach to identify anomalies in the SGW elements of a LTE network. Unsupervised techniques were compared and used to identify and remove anomalies in the training data set. This “cleaned” data set was then used to train an autoe...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Statistical Sciences
2020
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
Similar Items: Anomaly detection in a mobile data network
- Unsupervised Machine Learning for Detecting Structural Anomalies in European Regional Statistics
- Temporally Consistent Present Population from Mobile Network Signaling Data for Official Statistics
- On the theory and practice of anomaly detection in time series
- Understanding the low volatility anomaly in the South African equity market
- Horizon Detection in Seismic Data: An Application of Linked Feature Detection from Multiple Time Series
- Forecasting of Giresun Temperature and Pressure Data with Multilayer Artificial Neural Network