A Hybrid Framework for Time-series Analysis

  • Data is the new gold and serves as a key to answer the five W’s (Who, What, Where, When, Why) and How’s of any business. Companies are now mining data more than ever and one of the most important aspects while analyzing this data is to detect anomalous patterns to identify critical patterns and points. To tackle the vital aspects of timeseries analysis, this thesis presents a novel hybrid framework that stands on three pillars: Anomaly Detection, Uncertainty Estimation, and Interpretability and Explainability. The first pillar is comprised of contributions in the area of time-series anomaly detection. Deep Anomaly Detection for Time-series (DeepAnT), a novel deep learning-based anomaly detection method, lies at the foundation of the proposed hybrid framework and addresses the inadequacy of traditional anomaly detection methods. To the best of the author’s knowledge, Convolutional Neural Network (CNN) was used for the first time in Deep Anomaly Detection for Time-series (DeepAnT) to robustly detect multiple types of anomalies in the tricky and continuously changing time-series data. To further improve the anomaly detection performance, a fusion-based method, Fusion of Statistical and Deep Learning for Anomaly Detection (FuseAD) is proposed. This method aims to combine the strengths of existing wellfounded statistical methods and powerful data-driven methods. In the second pillar of this framework, a hybrid approach that combines the high accuracy of the deterministic models with the posterior distribution approximation of Bayesian neural networks is proposed. In the third pillar of the proposed framework, mechanisms to enable both HOW and WHY parts are presented.

Download full text files

Export metadata

Author:Mohsin MunirORCiD
Subtitle (English):From Anomaly Detection to Uncertainty Estimation and Explainability
Advisor:Andreas Dengel
Document Type:Doctoral Thesis
Language of publication:English
Publication Date:2021/12/22
Year of Publication:2021
Publishing Institute:Technische Universität Kaiserslautern
Granting Institute:Technische Universität Kaiserslautern
Acceptance Date of the Thesis:2021/08/19
Date of the Publication (Server):2021/12/22
Number of page:XIV, 198
Faculties / Organisational entities:Fachbereich Informatik
DDC-Cassification:0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):Creative Commons 4.0 - Namensnennung, nicht kommerziell (CC BY-NC 4.0)