Forecasting Trends in Time Series

Gardner, Everette S.; McKenzie, E.

doi:10.1287/mnsc.31.10.1237

Cited by 334 publications

(157 citation statements)

References 2 publications

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“…Gardner (1985) showed that trend damping improves accuracy for extrapolation models. Gardner's exponential smoothing with damped trends was tested in a study involving the 1,001 series from the M-Competition (Gardner and McKenzie, 1985). Trend damping reduced forecast errors by 10.5% on average over forecast horizons 1-18 when compared with traditional exponential smoothing with a linear trend (Makridakis et al, 1982).…”

Section: Damped Trendmentioning

confidence: 99%

Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong

2006

International Journal of Forecasting

151

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Empirical comparisons of reasonable approaches provide evidence on the best forecasting procedures to use under given conditions. Based on this evidence, I summarize the progress made over the past quarter century with respect to methods for reducing forecasting error. Seven well-established methods have been shown to improve accuracy: combining forecasts and Delphi help for all types of data; causal modeling, judgmental bootstrapping and structured judgment help with cross-sectional data; and causal models and trend-damping help with time-series data. Promising methods for cross-sectional data include damped causality, simulated interaction, structured analogies, and judgmental decomposition; for time-series data, they include segmentation, rule-based forecasting, damped seasonality, decomposition by causal forces, damped trend with analogous data, and damped seasonality. The testing of multiple hypotheses has also revealed methods where gains are limited: these include data mining, neural nets, and Box-Jenkins methods. Multiple hypotheses tests should be conducted on widely used but relatively untested methods such as prediction markets, conjoint analysis, diffusion models, and game theory. KeywordsBox-Jenkins, causal forces, causal models, combining forecasts, complex series, conjoint analysis, contrary series, damped seasonality, damped trend, data mining, Delphi, diffusion, game theory, judgmental decomposition, multiple hypotheses, neural nets, prediction markets, rule-based forecasting, segmentation, simulated interaction, structured analogies AbstractEmpirical comparisons of reasonable approaches provide evidence on the best forecasting

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Section: Damped Trendmentioning

confidence: 99%

Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong

2006

International Journal of Forecasting

151

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“…Despite its popularity, empirical evidence has shown that the Holt linear forecast function tends to overestimate (Gardner and McKenzie, 1985). In view of this, Gardner and McKenzie (1985) describe how a dampening parameter, φ, can be used within Holt's method to give more control over trend extrapolation. The damped Holt method is presented in expressions (4)-(6).…”

Section: Exponential Smoothing With Additive and Multiplicative Trendsmentioning

confidence: 99%

“…Chatfield and Yar (1988) Makridakis et al, 1982). Another approach is to use a simple linear regression on time to produce initial slope and trend (Gardner and McKenzie, 1985). Our experience is that this can give poor results for series that are notably different to a simple linear trend.…”

Section: Description Of the Studymentioning

confidence: 99%

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Exponential smoothing with a damped multiplicative trend

Taylor

2003

International Journal of Forecasting

229

114

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Multiplicative trend exponential smoothing has received very little attention in the literature.It involves modelling the local slope by smoothing successive ratios of the local level, and this leads to a forecast function that is the product of level and growth rate. By contrast, the popular Holt method uses an additive trend formulation. It has been argued that more real series have multiplicative trends than additive. However, even if this is true, it seems likely that the more conservative forecast function of the Holt method will be more robust when applied in an automated way to a large batch of series with different types of trend. In view of the improvements in accuracy seen in dampening the Holt method, in this paper, we investigate a new damped multiplicative trend approach. An empirical study, using the monthly time series from the M3-Competition, gave encouraging results for the new approach at a range of forecast horizons, when compared to the established exponential smoothing methods.

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“…The Management Science articles primarily discussed combining methods, including the role of judgement. Two articles, Gardner and McKenzie (1985) and Collopy and Armstrong (1992), proposed new extrapolative …”

Section: Introductionmentioning

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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Forecasting Trends in Time Series

Cited by 334 publications

References 2 publications

Findings from evidence-based forecasting: Methods for reducing forecast error

Findings from evidence-based forecasting: Methods for reducing forecast error

Exponential smoothing with a damped multiplicative trend

Forecasting and operational research: a review

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