Design of a machine learning application for anomaly detection in real-time series

Rodríguez Martínez, Carmelo (2019). Design of a machine learning application for anomaly detection in real-time series. Thesis (Master thesis), E.T.S.I. Telecomunicación (UPM).

Description

Title: Design of a machine learning application for anomaly detection in real-time series
Author/s:
  • Rodríguez Martínez, Carmelo
Contributor/s:
  • García Puga, Javier
Item Type: Thesis (Master thesis)
Masters title: Ingeniería de Telecomunicación
Date: 2019
Subjects:
Freetext Keywords: anomaly, connection, data, dataset, device, IoT, LSTM, machine learning, method, network, prediction, python, SIM, stationarity, technique, Telef´onica, time-series data, visualization
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (3MB) | Preview

Abstract

In nearly all enterprises, time series-connected problems are a day-to-day issue which we should know how to face. The question to solve, what will happen to our metrics in the future, can only be answered using sophisticated prediction methods, such as the ones that will be studied in this project. There are numerous algorithms that can be applied to nearly every possible situation but, depending on some factors such as required quality of the prediction, length of the forecasted period, time and associated cost; we should be capable of selecting the appropriate method to successfully fulfil our needs. Time series data is a series of data points indexed in time order. Therefore, data is organized around relatively deterministic timestamps which, compared to random samples, may contain additional information that will be tried to be extracted. The objective of this project is, firstly, to study and understand the properties of time series data, with the intention of being capable of generate accurate predictions using different machine learning techniques. Once this is achieved, these methods will be combined with more sophisticated predictive algorithms with the intention of detecting anomalies in real time series datasets. In order to make this research, the IoT Department of Telefonica I+D has facilitated anonymised data of real clients, which will be used for testing all the methods and techniques presented along this project.

More information

Item ID: 55787
DC Identifier: http://oa.upm.es/55787/
OAI Identifier: oai:oa.upm.es:55787
Deposited by: Biblioteca ETSI Telecomunicación
Deposited on: 10 Jul 2019 12:17
Last Modified: 11 Jul 2019 13:27
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM