Forecasting of Signals by Forecasting Linear Recurrence Relations

Standard

Forecasting of Signals by Forecasting Linear Recurrence Relations. / Golyandina, Nina ; Shapoval, Egor.

In: Engineering Proceedings, Vol. 39, No. 1, 28.06.2023.

Research output: Contribution to journal › Conference article

BibTeX

@article{c674e7da9f0c4f7f8b68d72713347b68,

title = "Forecasting of Signals by Forecasting Linear Recurrence Relations",

abstract = "The forecasting of a signal that locally satisfies linear recurrence relations (LRRs) with slowly changing coefficients is considered. A method that estimates the local LRRs using the subspace-based method, predicts their coefficients and constructs a forecast using the LRR with the predicted coefficients is proposed. This method is implemented for time series that have the form of a noisy sum of sine waves with modulated frequencies. Linear and sinusoidal frequency modulations are considered. The application of the algorithm is demonstrated with numerical examples.",

keywords = "time series, SIGNAL, forecasting, singular spectrum analysis, linear recurrence relation",

author = "Nina Golyandina and Egor Shapoval",

note = "Golyandina, N.; Shapoval, E. Forecasting of Signals by Forecasting Linear Recurrence Relations. Eng. Proc. 2023, 39, 12. https://doi.org/10.3390/engproc2023039012; null ; Conference date: 12-07-2023 Through 14-07-2023",

year = "2023",

month = jun,

day = "28",

doi = "10.3390/engproc2023039012",

language = "English",

volume = "39",

journal = "Engineering Proceedings",

issn = "2673-4591",

publisher = "MDPI AG",

number = "1",

url = "https://itise.ugr.es/",

}

RIS

TY - JOUR

T1 - Forecasting of Signals by Forecasting Linear Recurrence Relations

AU - Golyandina, Nina

AU - Shapoval, Egor

N1 - Conference code: 9

PY - 2023/6/28

Y1 - 2023/6/28

N2 - The forecasting of a signal that locally satisfies linear recurrence relations (LRRs) with slowly changing coefficients is considered. A method that estimates the local LRRs using the subspace-based method, predicts their coefficients and constructs a forecast using the LRR with the predicted coefficients is proposed. This method is implemented for time series that have the form of a noisy sum of sine waves with modulated frequencies. Linear and sinusoidal frequency modulations are considered. The application of the algorithm is demonstrated with numerical examples.

AB - The forecasting of a signal that locally satisfies linear recurrence relations (LRRs) with slowly changing coefficients is considered. A method that estimates the local LRRs using the subspace-based method, predicts their coefficients and constructs a forecast using the LRR with the predicted coefficients is proposed. This method is implemented for time series that have the form of a noisy sum of sine waves with modulated frequencies. Linear and sinusoidal frequency modulations are considered. The application of the algorithm is demonstrated with numerical examples.

KW - time series

KW - SIGNAL

KW - forecasting

KW - singular spectrum analysis

KW - linear recurrence relation

UR - https://www.mendeley.com/catalogue/e7ad2530-6282-3f29-8314-9a89ca611a4f/

U2 - 10.3390/engproc2023039012

DO - 10.3390/engproc2023039012

M3 - Conference article

VL - 39

JO - Engineering Proceedings

JF - Engineering Proceedings

SN - 2673-4591

IS - 1

Y2 - 12 July 2023 through 14 July 2023

ER -

ID: 107406906