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Complex Exponential Smoothing for Seasonal Time Series

Research output: Working paper

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Complex Exponential Smoothing for Seasonal Time Series. / Svetunkov, Ivan; Kourentzes, Nikolaos.

Lancaster : Department of Management Science, Lancaster University, 2018. p. 1-20.

Research output: Working paper

Harvard

Svetunkov, I & Kourentzes, N 2018 'Complex Exponential Smoothing for Seasonal Time Series' Department of Management Science, Lancaster University, Lancaster, pp. 1-20.

APA

Svetunkov, I., & Kourentzes, N. (2018). Complex Exponential Smoothing for Seasonal Time Series. (pp. 1-20). Department of Management Science, Lancaster University.

Vancouver

Svetunkov I, Kourentzes N. Complex Exponential Smoothing for Seasonal Time Series. Lancaster: Department of Management Science, Lancaster University. 2018 Jan, p. 1-20.

Author

Svetunkov, Ivan ; Kourentzes, Nikolaos. / Complex Exponential Smoothing for Seasonal Time Series. Lancaster : Department of Management Science, Lancaster University, 2018. pp. 1-20

Bibtex

@techreport{cdfa1fd75df64f9f97629457ffd4333c,
title = "Complex Exponential Smoothing for Seasonal Time Series",
abstract = "The general seasonal Complex Exponential Smoothing (CES) model is presented in this paper. CES is based on conventional exponential smoothing and a theory of complex variables. The proposed seasonal CES can capture known forms of seasonality, as well as new ones that are neither strictly additive nor multiplicative. In contrast to exponential smoothing, CES can capture both stationary and non-stationary processes, giving it greater modelling flexibility. In order to choose between the seasonal and non-seasonal CES a model selection procedure is discussed in the paper. An empirical evaluation of the performance of the model, against ETS and ARIMA, on real data is carried out. The findings suggest that CES simplifies model selection, and as a result the forecasting process, while performing better than the benchmarks in terms of forecasting accuracy.",
keywords = "Forecasting, Exponential Smoothing, complex variables, Seasonality",
author = "Ivan Svetunkov and Nikolaos Kourentzes",
year = "2018",
month = jan,
language = "English",
volume = "2018:1",
pages = "1--20",
publisher = "Department of Management Science, Lancaster University",
type = "WorkingPaper",
institution = "Department of Management Science, Lancaster University",

}

RIS

TY - UNPB

T1 - Complex Exponential Smoothing for Seasonal Time Series

AU - Svetunkov, Ivan

AU - Kourentzes, Nikolaos

PY - 2018/1

Y1 - 2018/1

N2 - The general seasonal Complex Exponential Smoothing (CES) model is presented in this paper. CES is based on conventional exponential smoothing and a theory of complex variables. The proposed seasonal CES can capture known forms of seasonality, as well as new ones that are neither strictly additive nor multiplicative. In contrast to exponential smoothing, CES can capture both stationary and non-stationary processes, giving it greater modelling flexibility. In order to choose between the seasonal and non-seasonal CES a model selection procedure is discussed in the paper. An empirical evaluation of the performance of the model, against ETS and ARIMA, on real data is carried out. The findings suggest that CES simplifies model selection, and as a result the forecasting process, while performing better than the benchmarks in terms of forecasting accuracy.

AB - The general seasonal Complex Exponential Smoothing (CES) model is presented in this paper. CES is based on conventional exponential smoothing and a theory of complex variables. The proposed seasonal CES can capture known forms of seasonality, as well as new ones that are neither strictly additive nor multiplicative. In contrast to exponential smoothing, CES can capture both stationary and non-stationary processes, giving it greater modelling flexibility. In order to choose between the seasonal and non-seasonal CES a model selection procedure is discussed in the paper. An empirical evaluation of the performance of the model, against ETS and ARIMA, on real data is carried out. The findings suggest that CES simplifies model selection, and as a result the forecasting process, while performing better than the benchmarks in terms of forecasting accuracy.

KW - Forecasting

KW - Exponential Smoothing

KW - complex variables

KW - Seasonality

M3 - Working paper

VL - 2018:1

SP - 1

EP - 20

BT - Complex Exponential Smoothing for Seasonal Time Series

PB - Department of Management Science, Lancaster University

CY - Lancaster

ER -