Situational Factors Affecting Forecast Accuracy

Schnaars, Steven P.

doi:10.2307/3151605

Cited by 52 publications

(25 citation statements)

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“…Armstrong (1985) provided an early review of the evidence on how to forecast given high uncertainty. Schnaars (1984) and Schnaars and Bavuso (1986) concluded that the random walk was typically the most accurate model in their comparative studies of hundreds of economic series with forecast horizons of up to five years. This principle has a long history.…”

Section: Discussionmentioning

confidence: 99%

Polar Bear Population Forecasts: A Public-Policy Forecasting Audit

2008

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Section: Discussionmentioning

confidence: 99%

Polar Bear Population Forecasts: A Public-Policy Forecasting Audit

2008

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“…Not all evidence supports the principal, however. Schnaars (1984) concluded that more data did not improve accuracy significantly in extrapolations of annual consumer product sales. While the evidence is mixed, accuracy sometimes suffers when analysts use too few data, so it seems best, in general, to use all relevant data.…”

Section: Selecting and Preparing Datamentioning

confidence: 99%

Extrapolation for Time-Series and Cross-Sectional Data

Armstrong

2001

International Series in Operations Research &Amp; Management Science

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Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts' commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased. Disciplines Business | Marketing ABSTRACTExtrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts' commonly held opinions) and on empirical studies. Some of the more important principles are:

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“…The shorter the series, the greater the uncertainty. In an (unpublished) analysis using annual sales data provided by Schnaars (1984), we found it difficult to improve accuracy over that provided by a simple rule: "if there are fewer than eight observations, use the random walk. "…”

Section: Testing In New Situationsmentioning

confidence: 99%

“…These included ten annual series on epidemics in China, quarterly personnel data for nine US Navy pay grades, annual unit product sales for 51 consumer products (based on a 50 percent probability sample from Schnaars 1984), and 26 economic and demographic series from a variety of published sources that were collected at the Weatherhead School at Case Western Reserve University. In each data set, the trend extrapolation of contrary series produced forecast errors that were substantially larger than those from the random walk, all differences being significant at p < .05 (Exhibit 9).…”

Section: Exhibit 7 Forecasting Of Trends Depends On the Directions Ofmentioning

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Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation

Armstrong

Adya

Collopy

2001

International Series in Operations Research &Amp; Management Science

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Rule-Based Forecasting (RBF) is an expert system that uses judgment to develop and apply rules for combining extrapolations. The judgment comes from two sources, forecasting expertise and domain knowledge. Forecasting expertise is based on more than a half century of research. Domain knowledge is obtained in a structured way; one example of domain knowledge is managers= expectations about trends, which we call "causal forces. " Time series are described in terms of 28 conditions, which are used to assign weights to extrapolations. Empirical results on multiple sets of time series show that RBF produces more accurate forecasts than those from traditional extrapolation methods or equal-weights combined extrapolations. RBF is most useful when it is based on good domain knowledge, the domain knowledge is important, the series is well behaved (such that patterns can be identified), there is a strong trend in the data, and the forecast horizon is long. Under ideal conditions, the error for RBF's forecasts were one-third less than those for equal-weights combining. When these conditions are absent, RBF neither improves nor harms forecast accuracy. Some of RBF's rules can be used with traditional extrapolation procedures. In a series of studies, rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals. Disciplines Business | Marketing ABSTRACTRule-Based Forecasting (RBF) is an expert system that uses judgment to develop and apply rules for combining extrapolations. The judgment comes from two sources, forecasting expertise and domain knowledge. Forecasting expertise is based on more than a half century of research. Domain knowledge is obtained in a structured way; one example of domain knowledge is managers= expectations about trends, which we call "causal forces." Time series are described in terms of 28 conditions, which are used to assign weights to extrapolations. Empirical results on multiple sets of time series show that RBF produces more accurate forecasts than those from traditional extrapolation methods or equal-weights combined extrapolations. RBF is most useful when it is based on good domain knowledge, the domain knowledge is important, the series is well behaved (such that patterns can be identified), there is a strong trend in the data, and the forecast horizon is long. Under ideal conditions, the error for RBF's forecasts were one-third less than those for equal-weights combining. When these conditions are absent, RBF neither improves nor harms forecast accuracy. Some of RBF's rules can be used with traditional extrapolation procedures. In a series of studies, rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals.

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Situational Factors Affecting Forecast Accuracy

Cited by 52 publications

References 0 publications

Polar Bear Population Forecasts: A Public-Policy Forecasting Audit

Polar Bear Population Forecasts: A Public-Policy Forecasting Audit

Extrapolation for Time-Series and Cross-Sectional Data

Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation

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