Svetunkov, Ivan and Kourentzes, Nikolaos (2015): Complex Exponential Smoothing.
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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 various 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, ``Complex exponential smoothing" (CES), is proposed. It has an underlying statistical model described here and has several advantages over the conventional exponential smoothing models: it allows modelling and forecasting both trended and level time series, effectively sidestepping the model selection problem. CES is evaluated on real data demonstrating better performance than established benchmarks and other exponential smoothing methods.
Item Type: | MPRA Paper |
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Original Title: | Complex Exponential Smoothing |
Language: | English |
Keywords: | Forecasting, exponential smoothing, ETS, model selection, information potential, complex variables |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods |
Item ID: | 69394 |
Depositing User: | Mr Ivan Svetunkov |
Date Deposited: | 15 Feb 2016 16:50 |
Last Modified: | 26 Sep 2019 16:30 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/69394 |