Exponential smoothing in the telecommunications data

Gardner, Everette S.; Diaz-Saiz, Joaquin

doi:10.1016/j.ijforecast.2007.05.002

Cited by 24 publications

(15 citation statements)

References 14 publications

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“…Various approaches to individual selection have been appraised but have delivered only limited benefits, although the commercial software, ForecastPro ® (BFS, see Makridakis and Hibon, 2000), uses an expert system that performs well. However, when selection is applied to homogeneous data series as in the telecoms data set (Fildes et al, 1998) the gains can be substantial, even when compared to a strong benchmark such as damped trend smoothing (Gardner and Diaz-Saiz, 2008).…”

Section: Methods Selection and Forecasting Competitionsmentioning

confidence: 99%

Forecasting and operational research: a review

Fildes

Νικολόπουλος

Crone

et al. 2008

Journal of the Operational Research Society

225

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From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.

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Section: Methods Selection and Forecasting Competitionsmentioning

confidence: 99%

Forecasting and operational research: a review

Fildes

Νικολόπουλος

Crone

et al. 2008

Journal of the Operational Research Society

225

View full text Add to dashboard Cite

show abstract

“…Among contributions more recent one can cite: Xiong and Yeung (2004), Liao (2005), Gardner and Diaz-Saiz (2008), Zhang, Chen, Brijs, and Zhang (2008), Böttcher, Spott, Nauck, andKruse (2009), Gullo, Ponti, Tagarelli, andGreco (2009).…”

Section: A Brief Review Of the Literaturementioning

confidence: 99%

“…In this paper, a methodology to monitor the evolution of cluster models and their objects, after the application of an unsupervised classification on a TDB from a characterization of the time series, via Holt-Winters smoothing model (Gardner & Diaz-Saiz, 2008;Winters, 1960), that make up the database is proposed.…”

Section: A Brief Review Of the Literaturementioning

confidence: 99%

Characterization of time series for analyzing of the evolution of time series clusters

Serra¹,

Zárate²

2015

Expert Systems with Applications

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“…Given that absolute error is a more robust measure of error magnitude than squared error, it may be preferable to optimise exponential smoothing parameters by minimising the sum of absolute estimation sample errors, rather than the standard use of the sum of squared errors (see Gardner and Diaz-Saiz, 2008). We consider this in our analysis.…”

Section: Introductionmentioning

confidence: 99%

Multi-item sales forecasting with total and split exponential smoothing

Taylor

2011

Journal of the Operational Research Society

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Efficient supply chain management relies on accurate demand forecasting. Typically, forecasts are required at frequent intervals for many items. Forecasting methods suitable for this application are those that can be relied upon to produce robust and accurate predictions when implemented within an automated procedure. Exponential smoothing methods are a common choice. In this empirical case study paper, we evaluate a recently proposed seasonal exponential smoothing method that has previously been considered only for forecasting daily supermarket sales. We term this method 'total and split' exponential smoothing, and apply it to monthly sales data from a publishing company. The resulting forecasts are compared against a variety of methods, including several available in the software currently used by the company. Our results show total and split exponential smoothing outperforming the other methods considered. The results were also impressive for a method that trims outliers and then applies simple exponential smoothing.

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Exponential smoothing in the telecommunications data

Cited by 24 publications

References 14 publications

Forecasting and operational research: a review

Forecasting and operational research: a review

Characterization of time series for analyzing of the evolution of time series clusters

Multi-item sales forecasting with total and split exponential smoothing

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