Estimation, forecasting and anomaly detection for nonstationary streams using adaptive estimation
File(s)
Accepted version
Author(s)
Helfer Hoeltgebaum, Henrique
Adams, Niall
Fernandes, Cristiano
Type
Journal Article
Abstract
Streaming data provides substantial challenges for data analysis. From a computational standpoint, these challenges arise from constraints related to computer memory and processing speed. Statistically, the challenges relate to constructing procedures that can handle the so-called concept drift--the tendency of future data to have different underlying properties to current and historic data. The issue of handling structure, such as trend and periodicity, remains a difficult problem for streaming estimation. We propose the real-time adaptive component (RAC), a penalized-regression modeling framework that satisfies the computational constraints of streaming data, and provides the capability for dealing with concept drift. At the core of the estimation process are techniques from adaptive filtering. The RAC procedure adopts a specified basis to handle local structure, along with a least absolute shrinkage operator-like penalty procedure to handle over fitting. We enhance the RAC estimation procedure with a streaming anomaly detection capability. The experiments with simulated data suggest the procedure can be considered as a competitive tool for a variety of scenarios, and an illustration with real cyber-security data further demonstrates the promise of the method.
Date Issued
2021-03-11
Date Acceptance
2021-01-19
Citation
IEEE Transactions on Cybernetics, 2021, 52 (8), pp.7956-7967
ISSN
1083-4419
Publisher
Institute of Electrical and Electronics Engineers
Start Page
7956
End Page
7967
Journal / Book Title
IEEE Transactions on Cybernetics
Volume
52
Issue
8
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/9376695
Subjects
Science & Technology
Technology
Automation & Control Systems
Computer Science, Artificial Intelligence
Computer Science, Cybernetics
Computer Science
Estimation
Data models
Anomaly detection
Computational modeling
Adaptation models
Forecasting
Adaptive estimation
Adaptive filtering
anomaly detection
data stream
forgetting factor (FF)
time-varying sparsity
GENERALIZED LINEAR-MODELS
REGULARIZATION
REGRESSION
DRIFTS
Algorithms
0102 Applied Mathematics
0801 Artificial Intelligence and Image Processing
0906 Electrical and Electronic Engineering
Artificial Intelligence & Image Processing
Publication Status
Published
Date Publish Online
2021-03-11