Prediction and tracking of long-range-dependent sequences

Erhan Bayraktar; H. Vincent Poor; Raghuveer Rao

doi:10.1016/j.sysconle.2005.03.004

Prediction and tracking of long-range-dependent sequences

Erhan Bayraktar, H. Vincent Poor, Raghuveer Rao

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

Long-range-dependent (LRD) sequences have been found to be of importance in various fields such as telecommunications, signal processing and finance. Since the history of an LRD sequence has significant impact on the present values, it is expected that accurate prediction and tracking of these sequences are easier than of short-range-dependent sequences. The purpose of this paper is to verify whether distant observations in the past might increase the performance of a constrained tracker significantly when this information from the past is used in combination with recent observations.

Original language	English (US)
Pages (from-to)	1083-1090
Number of pages	8
Journal	Systems and Control Letters
Volume	54
Issue number	11
DOIs	https://doi.org/10.1016/j.sysconle.2005.03.004
State	Published - Nov 2005
Externally published	Yes

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
General Computer Science
Mechanical Engineering
Electrical and Electronic Engineering

Keywords

Fractional Gaussian noise
Long range dependence
Tracking problems

Access to Document

10.1016/j.sysconle.2005.03.004

Cite this

@article{d3002cdce640486eb22971fc3c6f423c,

title = "Prediction and tracking of long-range-dependent sequences",

abstract = "Long-range-dependent (LRD) sequences have been found to be of importance in various fields such as telecommunications, signal processing and finance. Since the history of an LRD sequence has significant impact on the present values, it is expected that accurate prediction and tracking of these sequences are easier than of short-range-dependent sequences. The purpose of this paper is to verify whether distant observations in the past might increase the performance of a constrained tracker significantly when this information from the past is used in combination with recent observations.",

keywords = "Fractional Gaussian noise, Long range dependence, Tracking problems",

author = "Erhan Bayraktar and Poor, {H. Vincent} and Raghuveer Rao",

year = "2005",

month = nov,

doi = "10.1016/j.sysconle.2005.03.004",

language = "English (US)",

volume = "54",

pages = "1083--1090",

journal = "Systems and Control Letters",

issn = "0167-6911",

publisher = "Elsevier B.V.",

number = "11",

}

TY - JOUR

T1 - Prediction and tracking of long-range-dependent sequences

AU - Bayraktar, Erhan

AU - Poor, H. Vincent

AU - Rao, Raghuveer

PY - 2005/11

Y1 - 2005/11

N2 - Long-range-dependent (LRD) sequences have been found to be of importance in various fields such as telecommunications, signal processing and finance. Since the history of an LRD sequence has significant impact on the present values, it is expected that accurate prediction and tracking of these sequences are easier than of short-range-dependent sequences. The purpose of this paper is to verify whether distant observations in the past might increase the performance of a constrained tracker significantly when this information from the past is used in combination with recent observations.

AB - Long-range-dependent (LRD) sequences have been found to be of importance in various fields such as telecommunications, signal processing and finance. Since the history of an LRD sequence has significant impact on the present values, it is expected that accurate prediction and tracking of these sequences are easier than of short-range-dependent sequences. The purpose of this paper is to verify whether distant observations in the past might increase the performance of a constrained tracker significantly when this information from the past is used in combination with recent observations.

KW - Fractional Gaussian noise

KW - Long range dependence

KW - Tracking problems

UR - http://www.scopus.com/inward/record.url?scp=24944534811&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=24944534811&partnerID=8YFLogxK

U2 - 10.1016/j.sysconle.2005.03.004

DO - 10.1016/j.sysconle.2005.03.004

M3 - Article

AN - SCOPUS:24944534811

SN - 0167-6911

VL - 54

SP - 1083

EP - 1090

JO - Systems and Control Letters

JF - Systems and Control Letters

IS - 11

ER -

Prediction and tracking of long-range-dependent sequences

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this