Top-down or bottom-up: Aggregate versus disaggregate extrapolations

Dangerfield, Byron; Morris, John S.

doi:10.1016/0169-2070(92)90121-o

Cited by 111 publications

(70 citation statements)

References 10 publications

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“…Dangerfield and Morris (1992) used exponential smoothing models to forecast all 15,753 unique series derived by aggregating pairs of the 178 monthly time-series used in the M-Competition (Makridakis et al 1982) that included at least 48 observations in the specification set. The additive decomposition forecasts derived by combining forecasts from exponential smoothing models of the individual series were more accurate for 74 percent of two-item series.…”

Section: Decompose the Problem To Best Use Knowledge Information Anmentioning

confidence: 99%

Golden Rule of Forecasting: Be Conservative

2015

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This paper proposes a unifying theory of forecasting in the form of a Golden Rule of Forecasting. The Golden Rule is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek all knowledge relevant to the problem, and use methods that have been validated for the situation. A checklist of 28 guidelines is provided to implement the Golden Rule. This article's review of research found 150 experimental comparisons; all supported the guidelines. The average error reduction from following a single guideline (compared to common practice) was 28 percent. The Golden Rule Checklist helps forecasters to forecast more accurately, especially when the situation is uncertain and complex, and when bias is likely. Nonexperts who know the Golden Rule can identify dubious forecasts quickly and inexpensively. To date, ignorance of research findings, bias, sophisticated statistical procedures, and the proliferation of big data have led forecasters to violate the Golden Rule. As a result, despite major advances in forecasting methods, evidence that forecasting practice has improved over the past half-century is lacking.

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Section: Decompose the Problem To Best Use Knowledge Information Anmentioning

confidence: 99%

Golden Rule of Forecasting: Be Conservative

2015

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“…One of the commonly used methods for hierarchical/grouped time-series forecasting is the bottom-up method (e.g., Kinney, 1971;Dangerfield and Morris, 1992;Zellner and Tobias, 2000). This method involves first generating base forecasts for each series at the bottom level of the hierarchy and then aggregating these upwards to produce revised forecasts for the whole hierarchy.…”

Section: Bottom-up Methodsmentioning

confidence: 99%

Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods

Shang

2016

Popul Res Policy Rev

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Mortality rates are often disaggregated by different attributes, such as sex, state, education, religion or ethnicity. Forecasting mortality rates at the national and subnational levels plays an important role in making social policies associated with the national and sub-national levels. However, base forecasts at the sub-national levels may not add up to the forecasts at the national level. To address this issue, we consider the problem of reconciling mortality rate forecasts from the viewpoint of grouped time-series forecasting methods (Hyndman, Ahmed, Athanasopoulos and Shang, 2011).A bottom-up method and an optimal combination method are applied to produce point forecasts of infant mortality rates that are aggregated appropriately across the different levels of a hierarchy. We extend these two methods by considering the reconciliation of interval forecasts through a bootstrap procedure. Using the regional infant mortality rates in Australia, we investigate the one-step-ahead to 20-step-ahead point and interval forecast accuracies among the independent and these two grouped time-series forecasting methods. The proposed methods are shown to be useful for reconciling point and interval forecasts of demographic rates at the national and sub-national levels, and would be beneficial for government policy decisions regarding the allocations of current and future resources at both the national and sub-national levels.

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“…Some scholars advocate the top down approach (Grunfeld and Griliches, 1960;Barnea and Lakonishok, 1980;Gross and Sohl, 1990;Fliedner, 1999), whereas others disagree (Orcutt et al, 1968;Edwards and Orcutt, 1969;Dangerfield and Morris, 1992;Gordon et al, 1997;Weatherford et al, 2001) and, among other reasons, argue that the outperformance of bottom-up is due to the fact that information loss is substantial when aggregating time series; that is, the data is too aggregated to represent diverse demands (NOTE 6) . Some additional arguments that are often put forward by practitioners against topdown approaches have been summarised by Dawson (2013):…”

Section: Hierarchical Product Aggregationmentioning

confidence: 99%

Supply chain forecasting: Theory, practice, their gap and the future

Syntetos

Babai

Boylan

et al. 2016

European Journal of Operational Research

229

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HIGHLIGHTSThe literature on supply chain forecasting is critically reviewed;The process of involving the forecasting community towards that task is described;Gaps between theory and practice are identified; Data and software related issues are explicitly considered; Challenges are summarised followed by suggestions for further research. ABSTRACTSupply Chain Forecasting (SCF) goes beyond the operational task of extrapolating demand requirements at one echelon. It involves complex issues such as supply chain coordination and sharing of information between multiple stakeholders. Academic research in SCF has tended to neglect some issues that are important in practice. In areas of practical relevance, sound theoretical developments have rarely been translated into operational solutions or integrated in state-of-the-art decision support systems. Furthermore, many experience-driven heuristics are increasingly used in everyday business practices. These heuristics are not supported by substantive scientific evidence; however, they are sometimes very hard to outperform. This can be attributed to the robustness of these simple and practical solutions such as aggregation approaches for example (across time, customers and products).This paper provides a comprehensive review of the literature and aims at bridging the gap between the theory and practice in the existing knowledge base in SCF. We highlight the most promising approaches and suggest their integration in forecasting support systems. We discuss the current challenges both from a research and practitioner perspective and provide a research and application agenda for further work in this area. Finally, we make a contribution in the methodology underlying the preparation of review articles by means of involving the forecasting community in the process of deciding both the content and structure of this paper.

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Top-down or bottom-up: Aggregate versus disaggregate extrapolations

Cited by 111 publications

References 10 publications

Golden Rule of Forecasting: Be Conservative

Golden Rule of Forecasting: Be Conservative

Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods

Supply chain forecasting: Theory, practice, their gap and the future

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