Home > Research > Publications & Outputs > Complex exponential smoothing

Research

Associated organisational units

Electronic data

Keywords

View graph of relations

Complex exponential smoothing

Research output: Working paper

Published

Standard

Complex exponential smoothing. / Svetunkov, Ivan; Kourentzes, Nikos.
Lancaster: Lancaster University Management School, 2016. p. 1-31.

Research output: Working paper

Harvard

Svetunkov, I & Kourentzes, N 2016 'Complex exponential smoothing' Lancaster University Management School, Lancaster, pp. 1-31.

APA

Svetunkov, I., & Kourentzes, N. (2016). Complex exponential smoothing. (pp. 1-31). Lancaster University Management School.

Vancouver

Svetunkov I, Kourentzes N. Complex exponential smoothing. Lancaster: Lancaster University Management School. 2016 Jan 2, p. 1-31.

Author

Svetunkov, Ivan ; Kourentzes, Nikos. / Complex exponential smoothing. Lancaster : Lancaster University Management School, 2016. pp. 1-31

Bibtex

@techreport{003f13426da342a9924852529b484336,
title = "Complex exponential smoothing",
abstract = "Exponential smoothing has been one of the most popular forecasting methods for business and industry. Its simplicity and transparency have made it very attractive. Nonetheless, modelling and identifying trends has been met with mixed success, resulting in the development of different modifications of trend models. We present a new approach to time series modelling, using the notion of {"}information potential{"} and the theory of functions of complex variables. A new exponential smoothing method that uses this approach is proposed, the {"}Complex exponential smoothing{"} (CES). It has an underlying statistical model described in the paper and has several advantages in comparison with the customary exponential smoothing models, that allow CES to model and forecast effectively both trended and level time series, effectively overcoming the model selection problem.",
keywords = "Forecasting, exponential smoothing, ETS, model selection, information potential, complex variables",
author = "Ivan Svetunkov and Nikos Kourentzes",
year = "2016",
month = jan,
day = "2",
language = "English",
volume = "2015:1",
pages = "1--31",
publisher = "Lancaster University Management School",
type = "WorkingPaper",
institution = "Lancaster University Management School",

}

RIS

TY - UNPB

T1 - Complex exponential smoothing

AU - Svetunkov, Ivan

AU - Kourentzes, Nikos

PY - 2016/1/2

Y1 - 2016/1/2

N2 - Exponential smoothing has been one of the most popular forecasting methods for business and industry. Its simplicity and transparency have made it very attractive. Nonetheless, modelling and identifying trends has been met with mixed success, resulting in the development of different modifications of trend models. We present a new approach to time series modelling, using the notion of "information potential" and the theory of functions of complex variables. A new exponential smoothing method that uses this approach is proposed, the "Complex exponential smoothing" (CES). It has an underlying statistical model described in the paper and has several advantages in comparison with the customary exponential smoothing models, that allow CES to model and forecast effectively both trended and level time series, effectively overcoming the model selection problem.

AB - Exponential smoothing has been one of the most popular forecasting methods for business and industry. Its simplicity and transparency have made it very attractive. Nonetheless, modelling and identifying trends has been met with mixed success, resulting in the development of different modifications of trend models. We present a new approach to time series modelling, using the notion of "information potential" and the theory of functions of complex variables. A new exponential smoothing method that uses this approach is proposed, the "Complex exponential smoothing" (CES). It has an underlying statistical model described in the paper and has several advantages in comparison with the customary exponential smoothing models, that allow CES to model and forecast effectively both trended and level time series, effectively overcoming the model selection problem.

KW - Forecasting

KW - exponential smoothing

KW - ETS

KW - model selection

KW - information potential

KW - complex variables

M3 - Working paper

VL - 2015:1

SP - 1

EP - 31

BT - Complex exponential smoothing

PB - Lancaster University Management School

CY - Lancaster

ER -