PreprintPDF Available
Preprints and early-stage research may not have been peer reviewed yet.


Purpose: Commentary on M4-Competition and findings to assess the contribution of data models--such as from machine learning methods--to improving forecast accuracy. Methods: (1) Use prior knowledge on the relative accuracy of forecasts from validated forecasting methods to assess the M4 findings. (2) Use prior knowledge on forecasting principles and the scientific method to assess whether data models can be expected to improve accuracy relative to forecasts from previously validated methods under any conditions. Findings: Prior knowledge from experimental research is supported by the M4 findings that simple validated methods provided forecasts that are: (1) typically more accurate than those from complex and costly methods; (2) considerably more accurate than those from data models. Limitations: Conclusions were limited by incomplete hypotheses from prior knowledge such as would have permitted experimental tests of which methods, and which individual models, would be most accurate under which conditions. Implications: Data models should not be used for forecasting under any conditions. Forecasters interested in situations where much relevant data are available should use knowledge models.
Why didn’t experts pick M4-Competition winner?
J. Scott Armstrong
The Wharton School, University of Pennsylvania
and Ehrenberg-Bass Institute, University of South Australia
Kesten C. Green
University of South Australia Business School
and Ehrenberg-Bass Institute, University of South Australia
February 10, 2019 Version 10
Purpose: Commentary on M4-Competition and findings to assess the contribution of data models
such as from machine learning methods—to improving forecast accuracy.
Methods: (1) Use prior knowledge on the relative accuracy of forecasts from validated forecasting
methods to assess the M4 findings. (2) Use prior knowledge on forecasting principles and the scientific
method to assess whether data models can be expected to improve accuracy relative to forecasts from
previously validated methods under any conditions.
Findings: Prior knowledge from experimental research is supported by the M4 findings that simple
validated methods provided forecasts that are: (1) typically more accurate than those from complex and
costly methods; (2) considerably more accurate than those from data models.
Limitations: Conclusions were limited by incomplete hypotheses from prior knowledge such as
would have permitted experimental tests of which methods, and which individual models, would be most
accurate under which conditions.
Implications: Data models should not be used for forecasting under any conditions. Forecasters
interested in situations where much relevant data are available should use knowledge models.
“Facts that speak for themselves, talk in a very naive language”
Ragnar Frisch, Nobel Prize Lecture (1970, p. 16).
In the mid-1900s, there were two streams of thought about forecasting methods. One streamled by
econometricianswas concerned with developing causal modelsby using prior knowledge and
evidence from experiments. The other was led by statisticians, who were concerned with identifying
idealized “data generating processes” and with developing models from statistical relationships in data, in
the expectation that the resulting models would provide accurate forecasts.
Makridakis, Spiliotis, and Assimakopoulos (2018) report that there were six machine learning (ML)
models entered in the M4-Competition, and that the forecasts from those models were ranked 23, 37, 38,
48, 54, and 57. The median rank of the ML models was thus 43 out of the total of 60 models in the
competition, including the competition’s 10 benchmarks. The M4 authors concluded that, “The six pure
ML methods that were submitted in the M4 all performed poorly, with none of them being more accurate
than Comb and only one being more accurate than Naïve2” (p. 803), where Comb was the average of
single, Holt, and damped exponential smoothing forecaststhe competition’s “statistical benchmark”
and Naïve2 forecasts were from a random walk model with seasonal adjustment.
Makridakis, et al. (2018) make a distinction between the six ML models and the “statistical” models
in the competition, but do not describe what they mean by ML methods. We understand ML models to be
a kind of what we call “data model,” by which we mean models that that are developed using automated
processes to identify patterns in that data that are used to predict values that were unknown in the
development of the model. We exclude from our definition simple extrapolation methods that have been
validated for forecasting time-series datawhat we understand to be at least some of Makridakis, et al.’s
(2018) “statistical models”and include methods that use automated routines to derive models that
include potential “predictor” variables. Think of step-wise regression as an early data modeling method.
1. What Can Be Concluded About Data Models From M4?
One of the requirements of the scientific method is to test multiple reasonable hypotheses
(Chamberlin 1890). In the case of the M4-Competition (Makridakis, et al., 2018), that would involve
using prior knowledge to specify hypotheses on which methodi.e., not individual model or competition
entrywould, on average, provide the most accurate forecasts under each of the conditions that would
apply in the tests.
The conditions for the tests should be representative of the situations that are the subject of the
studyin other words, ecologically valid. The competition’s 100,000 time series are identified only by
their frequencye.g., weekly, monthly, yearlyand broad classificatione.g., macro, financial, or
industrial (see Makridakis, 2018a; and Makridakis, 2018b)—and so lack the contextual information that
forecasters would have available to them in practice.
The competition format is intended to attract entrants who believe that their model might be the best,
and so it was necessary to acknowledge an individual model as the “winner.” Drawing any scientific
conclusions about the single winner would, however, require hypotheses about the relative accuracies
under specified conditionsof the 50 models that were submitted to the competition. In sum, without the
relevant hypotheses, any discussion of the results for the individual models can only be speculation.
Another requirement for science is replication. Would the same model provide the most accurate
forecasts for another 100,000 series? Or even for a split of the 100,000 used in the Competition? And,
what if alternative error measures were used?
Regarding error measures, Makridakis et al. (2018) use two. They are the symmetrical mean absolute
percentage error (sMAPE), and a combination of that measure and the mean absolute scaled error
(MASE) in the form of the overall weighted average of the relative sMAPE and the relative MASE, or
OWA. On the basis of the OWA, only 17 of the entries (34%) beat the competition’s naïve benchmark.
One of those was the entry that produced the second most accurate forecastson the basis of sMAPE
by using models that were developed using the principles described in Armstrong and Green (2018) (from
a personal communication from Srihari Jaganathan.)
Not reported were the results on the basis of three error measures used in the M3-Competition
(Makridakis and Hibon, 2000): percent better, the median symmetric absolute percentage error, and the
median relative absolute error. Other measures that would be relevant are the cumulative relative absolute
error or CumRAE (Armstrong and Collopy 1992), and the unscaled mean bounded relative absolute error
or UMBRAE (Chen, Twycross, and Garibaldi, 2017.) Given that the ranking of the models is different
between the two measures that were reported in M4even though one of the measures (OWA) is an
average that includes the first measureit seems likely that other error measures, too, would produce
different orderings of the models. For example, the model that was ranked fourth in Table 1 (Makridakis
et al, 2018) provided the second most accurate forecasts on the basis of sMAPE. Different orderings
would be less likely if the relative accuracy of forecasts from broad classes of methods were being
compared, of course.
The M4-Competition provides experimental evidence that data models on average provide forecasts
that are less accurate than those from simple previously validated methods. That finding is consistent with
Keogh and Kasetty’s (2003) conclusion from tests of data models that used diverse data sets and
performance measures. They found insufficient evidence to conclude that the methods could be useful in
In the remainder of this commentary, we describe why data models cannot be relied upon to provide
usefully accurate forecasts in typical forecasting situations in which at least some contextual and causal
knowledge are available.
2. Do Data Models Comply with the Golden Rule of Forecasting?
The Golden Rule of forecasting is to “Be conservative by using prior knowledge about the situation
and about forecasting methods.” It was developed and tested by Armstrong, Green and Graefe (2015).
The Golden Rule paper provides 28 evidence-based guidelines, which were tested by reviewing all papers
that we could find with relevant evidence. The review identified 105 papers with 150 experimental
comparisons. All comparisons supported the guidelines. On average, ignoring a single guideline increased
forecast error by more than 40% on average. We were astonished by those findings.
One way to incorporate prior knowledge is to use the findings of experiments on which methods work
best for the type of situation being forecast. In addition, in practical forecasting situations, one can use
experts’ domain knowledge about the expected directions of trends. In an earlier M-Competition, these
two sources of knowledge were implemented by “Rule-based forecasting” (Collopy and Armstrong
Rule-based forecasting uses domain knowledge to select extrapolation models based on 28 conditions
of the data in order to produce combined extrapolation forecasts. It uses 99 simple rules to weight each of
the extrapolation methods. The six-year ahead ex-ante forecasts made by rule-based forecasting were 42%
less than to those from equal-weights combinations. Other sources of prior knowledge can be used such
as decomposition of time-series by level and change (Armstrong and Tessier, 2015) and by causal forces
(Armstrong and Collopy, 1993.)
3. Do Data Models Comply with Occam’s Razor?
Occam’s Razor, a principle that was described by Aristotle (Charlesworth, 1956), states that one
should prefer the simplest hypothesis, or model, that does the job. A review of 32 studies found 97
comparisons between simple and complex methods (Green and Armstrong, 2015.) None found that
complexity improved forecast accuracy. On the contrary, complexity increased errors by an average of
27% in the 25 papers with quantitative comparisons.
Unsurprisingly, then, all of the validated methods for forecasting are simple. For a checklist of
validated methods, see the Methods Checklist at
4. Data Models Enable Advocacy, Leading to Unscientific and, Potentially, Unethical Practices
The nature of data modeling procedures is such that researchers can develop data models to provide
the forecasts that they know their clients or sponsors would prefer. Doing so helps them to get grants and
promotions. They can also use them to support their own preferred hypotheses.
Traditionally, researchers have justified their models by claiming that they are statistically significant,
but it is not difficult to obtain statistically significant findings. And, of course, all tested relationships
become statistically significant if the sample sizes are large enough.
Despite the widespread use of statistical significance testing, the technique has never been validated.
On the contrary, decades of research have found that statistical significance testing harms scientific
advances. For example, Koning, et al. (2005) concluded that statistical tests showed that combining
forecasts did not reduce forecasting errors in the M3-Competition. Those findings were refuted in
Armstrong (2007).
In the case of newer data modeling methods, statistical significance tests are not used to justify the
models. Nevertheless, the complexity and opacity of the procedures, the data that are made available to
the procedures, and the resulting models’ makes it possible for modelers to select models that are
consistent with their preferred hypotheses.
5. Do Data Models Violate the Guidelines for Regression Analysis?
Data models are related to regression analysis in that the models are estimated only from the data the
modeling procedures are provided with. With that in mind, we rated data modeling procedures against the
Checklist for forecasting using regression analysis at By our ratings, typical
procedures for estimating data models violate at least 15 of the 18 guidelines in the checklist. For
example, they obviously violate the guidance to “select variables using prior knowledge, logic based on
known relationships, and experimental studies.” A summary of evidence on the limitations of regression
analysis is provided in Armstrong (2012).
6. Do data models (ML models) follow guidelines for the scientific method?
There are eight criteria that need to be met for scientific research. They are to: (1) study an important
problem, (2) use prior knowledge, (3) fully disclose hypotheses, data, and methods, (4) use an objective
design, (5) use valid and reliable data, (6) use validated methods, (7) use experimental evidence, and (8)
draw logical conclusions. We each independently rated “data models” using a 26-item “Compliance with
Science” checklist described in Armstrong and Green (2018.) Our combined assessment was that 7.5 out
of the 8 required criteria are violated by data modeling methods.
When Data are Plentiful Use Knowledge Models, not Data Models
Benjamin Franklin proposed a simple method that we now refer to as “knowledge models” and,
previously, as “index models.” The procedure is as follows:
a. Use domain knowledge to specify:
all important causal variables,
directions of their effects, and
when sufficient knowledge is available, the magnitudes of their relationships.
Variables should be scaled to relate positively to the thing that is being forecast.
There is no limit on the number of causal variables that can be used.
Variables may be as simple as binary, or “dummy” variables.
b. Use equal weights and standardized variables in the model unless there is strong evidence of
differences in relative effect sizes. Equal weights are often more accurate than regression
weights, especially when there are many variables and where prior knowledge about relative
effect sizes is poor.
c. Forecast the values of the causal variables in the model.
d. Apply the model weights to the forecast causal variable values and sum to calculate a score.
e. The score is a forecast. A higher score means that the thing being forecast is likely to be
better, greater, or more likely than would be the case for a lower score. If there are sufficient
data to do so, estimate a single regression model that relates scores from the model with the
actual values of the thing being forecast.
Evidence to date suggests that knowledge models are likely to produce forecasts that are more
accurate than those from data models in situations where many causal variables are important. One
knowledge model found error reductions of 10% to 43% compared to established regression models for
forecasting elections in the U.S. and Australia (Graefe, Green, and Armstrong, 2019).
In conclusion, science advances not by looking for evidence to support a favorite hypothesis, but by
using prior knowledge to propose hypotheses and to design experiments to test them in order to discover
useful principles and methods. Our suggestion for future competitions is that when competitors submit
their models and forecasts, they should use prior research to explain the principles and methods that they
used, and their prior hypotheses on relative accuracy of the methods under different conditions. That
would allow the competition organizers, commentators, and other researchers to compare the results by
method category, taking account of prior experimental evidence, rather than resorting to ex post
speculation on what can be learned from the results.
Armstrong, J. S. (2007). Significance Tests Harm Progress in forecasting. International Journal of
Forecasting, 23, 321-336.
Armstrong, J. S. (2012). Illusions in regression analysis, International Journal of Forecasting, 28, 689-
Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods:
Empirical comparisons. International Journal of Forecasting, 8, 6980.
Armstrong, J. S. & Collopy, F. (1993). Causal Forces: Structuring Knowledge for Time-series
Extrapolation, Journal of Forecasting, 12, 103-115.
Armstrong, J. S., Collopy, F. & Yokum, J. T. (2005). Decomposition by Causal Forces: A Procedure for
Forecasting Complex Time Series, International Journal of Forecasting, 21 (2005), 25-36.
Armstrong, J. S. & Green, K. C. (2018). Guidelines for Science: Evidence-based checklists. Working
Paper, DOI: 10.2139/ssrn.3055874
Armstrong, J. S. & Green, K. C. (2018). Forecasting methods and principles: Evidence-based checklists,
Journal of Global Scholars in Marketing Science, 28, 103-159.
Armstrong, J. S, Green, K. C., & Graefe, A. (2015). Golden rule of Forecasting: Be Conservative. Journal
of Business Research, 68, 1717-1731.
Armstrong, J. S. & Tessier, T. (2015). Decomposition of time-series by level and change, Journal of
Business Research, 68 (2015), 1755-1758.
Chamberlin, T. C. (1890). The method of multiple working hypotheses. Reprinted in 1965 in Science,
148, 754-759.
Charlesworth, M. J. (1956). Aristotle’s razor. Philosophical Studies, 6, 105112.
Chen, C., Twycross, J., & Garibaldi, J. M. (2017). A new accuracy measure based on bounded relative
error for time series forecasting. PLoS ONE, 12(3): e0174202.
Collopy, F. & Armstrong, J. S. (1992). Rule-Based Forecasting: Development and Validation of an
Expert Systems Approach to Combining Time Series Extrapolations, Management Science, 38,1394-
Einhorn, H. (1972). Alchemy in the behavioral sciences. Public Opinion Quarterly, 36, 367-378.
Graefe, A., Green, K. C. & Armstrong, J. S. (2019). Accuracy gains from conservative forecasting:
Tests using variations of 19 econometric models to predict 154 elections in 10 countries. PLoS
ONE, 14(1), e0209850.
Frisch, R. (1970). From Utopian Theory to Practical Applications: The Case of Econometrics. Lecture
to the Memory of Alfred Nobel, June 17, 1970. Nobel Media AB,
Green, K. C. & Armstrong, J. S. (2015). Simple versus complex forecasting: The evidence. Journal of
Business Research, 68, 1678-1685.
Keogh, E. & Kasetty, S. (2003). On the need for time series data mining benchmarks: A survey and
empirical demonstration. Data Mining and Knowledge Discovery, 7, 349371.
Koning, A.J., Franses, P. H., Hibon, M., & Stekler, H. O..(2005). The M3-Competition: Statistical
tests of the results. International Journal of Forecasting, 21, 397-409.
Makridakis, S. (2018). The Dataset. University of Nicosia,
Makridakis, S. (2018). Info. University of Nicosia,
Makridakis, S., & Hibon, M. (2000). The M3-Competition: results, conclusions and implications.
International Journal of Forecasting, 16, 451-476.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). The M4 Competition: Results, findings,
conclusion and way forward. International Journal of Forecasting, 34, 802-808.
Text, excluding abstract = 2,132
References 467
Acknowledgements: We thank Robert Fildes, Andreas Graefe, Eamon Keogh, and an anonymous
reviewer for reviewing drafts of this paper.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Problem Do conservative econometric models that comply with the Golden Rule of Forecasting pro- vide more accurate forecasts? Methods To test the effects of forecast accuracy, we applied three evidence-based guidelines to 19 published regression models used for forecasting 154 elections in Australia, Canada, Italy, Japan, Netherlands, Portugal, Spain, Turkey, U.K., and the U.S. The guidelines direct fore- casters using causal models to be conservative to account for uncertainty by (I) modifying effect estimates to reflect uncertainty either by damping coefficients towards no effect or equalizing coefficients, (II) combining forecasts from diverse models, and (III) incorporating more knowledge by including more variables with known important effects. Findings Modifying the econometric models to make them more conservative reduced forecast errors compared to forecasts from the original models: (I) Damping coefficients by 10% reduced error by 2% on average, although further damping generally harmed accuracy; modifying coefficients by equalizing coefficients consistently reduced errors with average error reduc- tions between 2% and 8% depending on the level of equalizing. Averaging the original regression model forecast with an equal-weights model forecast reduced error by 7%. (II) Combining forecasts from two Australian models and from eight U.S. models reduced error by 14% and 36%, respectively. (III) Using more knowledge by including all six unique vari- ables from the Australian models and all 24 unique variables from the U.S. models in equal- weight “knowledge models” reduced error by 10% and 43%, respectively. Originality This paper provides the first test of applying guidelines for conservative forecasting to estab- lished election forecasting models. Usefulness Election forecasters can substantially improve the accuracy of forecasts from econometric models by following simple guidelines for conservative forecasting. Decision-makers can make better decisions when they are provided with models that are more realistic and fore- casts that are more accurate.
Full-text available
The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method's average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.
Full-text available
Problem How to help practitioners, academics, and decision makers use experimental research findings to substantially reduce forecast errors for all types of forecasting problems. Methods Findings from our review of forecasting experiments were used to identify methods and principles that lead to accurate forecasts. Cited authors were contacted to verify that summaries of their research were correct. Checklists to help forecasters and their clients undertake and commission studies that adhere to principles and use valid methods were developed. Leading researchers were asked to identify errors of omission or commission in the analyses and summaries of research findings. Findings Forecast accuracy can be improved by using one of 15 relatively simple evidence-based forecasting methods. One of those methods, knowledge models, provides substantial improvements in accuracy when causal knowledge is good. On the other hand, data models – developed using multiple regression, data mining, neural nets, and “big data analytics” – are unsuited for forecasting. Originality Three new checklists for choosing validated methods, developing knowledge models, and assessing uncertainty are presented. A fourth checklist, based on the Golden Rule of Forecasting, was improved. Usefulness Combining forecasts within individual methods and across different methods can reduce forecast errors by as much as 50%. Forecasts errors from currently used methods can be reduced by increasing their compliance with the principles of conservatism (Golden Rule of Forecasting) and simplicity (Occam’s Razor). Clients and other interested parties can use the checklists to determine whether forecasts were derived using evidence-based procedures and can, therefore, be trusted for making decisions. Scientists can use the checklists to devise tests of the predictive validity of their findings.
Full-text available
Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising commonly used accuracy measures, a special review is made on the symmetric mean absolute percentage error. Moreover, a new accuracy measure called the Unscaled Mean Bounded Relative Absolute Error (UMBRAE), which combines the best features of various alternative measures, is proposed to address the common issues of existing measures. A comparative evaluation on the proposed and related measures has been made with both synthetic and real-world data. The results indicate that the proposed measure, with user selectable benchmark, performs as well as or better than other measures on selected criteria. Though it has been commonly accepted that there is no single best accuracy measure, we suggest that UMBRAE could be a good choice to evaluate forecasting methods, especially for cases where measures based on geometric mean of relative errors, such as the geometric mean relative absolute error, are preferred.
Full-text available
Problem: The scientific method is unrivaled for generating useful knowledge, yet papers published in scientific journals frequently violate the scientific method. Methods: A definition of the scientific method was developed from the writings of pioneers of the scientific method including Aristotle, Newton, and Franklin. The definition was used as the basis of a checklist of eight criteria necessary for compliance with the scientific method. The extent to which research papers follow the scientific method was assessed by reviewing the literature on the practices of researchers whose papers are published in scientific journals. Findings of the review were used to develop an evidence-based checklist of 20 operational guidelines to help researchers comply with the scientific method. Findings: The natural desire to have one’s beliefs and hypotheses confirmed can tempt funders to pay for supportive research and researchers to violate scientific principles. As a result, advocacy has come to dominate publications in scientific journals, and had led funders, universities, and journals to evaluate researchers’ work using criteria that are unrelated to the discovery of useful scientific findings. The current procedure for mandatory journal review has led to censorship of useful scientific findings. We suggest alternatives, such as accepting all papers that conform with the eight critera of the scientific method. Originality: This paper provides the first comprehensive and operational evidence-based checklists for assessing compliance with the scientific method and for guiding researchers on how to comply. Usefulness: The “Criteria for Compliance with the Scientific Method” checklist could be used by journals to certify papers. Funders could insist that research projects comply with the scientific method. Universities and research institutes could hire and promote researchers whose research complies. Courts could use it to assess the quality of evidence. Governments could base policies on evidence from papers that comply, and citizens could use the checklist to evaluate evidence on public policy. Finally, scientists could ensure that their own research complies with science by designing their projects using the “Guidelines for Scientists” checklist. Keywords: advocacy; checklists; data models; experiment; incentives; knowledge models; multiple reasonable hypotheses; objectivity; regression analysis; regulation; replication; statistical significance
Full-text available
This article examines whether decomposing time series data into two parts - level and change - produces forecasts that are more accurate than those from forecasting the aggregate directly. Prior research found that, in general, decomposition reduced forecasting errors by 35%. An earlier study on decomposition into level and change found a forecast error reduction of 23%. The current study found that nowcasts consisting of a simple average of estimates from preliminary surveys and econometric models of the U.S. lodging market, improved the accuracy of final estimates of levels. Forecasts of change from an econometric model and the improved nowcasts reduced forecast errors by 29% when compared to direct forecasts of the aggregate. Forecasts of change from an extrapolation model and the improved nowcasts reduced forecast errors by 45%. On average then, the error reduction for this study was 37%.
Full-text available
This article introduces this JBR Special Issue on simple versus complex methods in forecasting. Simplicity in forecasting requires that (1) method, (2) representation of cumulative knowledge, (3) relationships in models, and (4) relationships among models, forecasts, and decisions are all sufficiently uncomplicated as to be easily understood by decision-makers. Our review of studies comparing simple and complex methods - including those in this special issue - found 97 comparisons in 32 papers. None of the papers provide a balance of evidence that complexity improves forecast accuracy. Complexity increases forecast error by 27 percent on average in the 25 papers with quantitative comparisons. The finding is consistent with prior research to identify valid forecasting methods: all 22 previously identified evidence-based forecasting procedures are simple. Nevertheless, complexity remains popular among researchers, forecasters, and clients. Some evidence suggests that the popularity of complexity may be due to incentives: (1) researchers are rewarded for publishing in highly ranked journals, which favor complexity; (2) forecasters can use complex methods to provide forecasts that support decision-makers’ plans; and (3) forecasters’ clients may be reassured by incomprehensibility. Clients who prefer accuracy should accept forecasts only from simple evidence-based procedures. They can rate the simplicity of forecasters’ procedures using the questionnaire at
Full-text available
This article proposes a unifying theory, or the Golden Rule, of forecasting. The Golden Rule of Forecasting is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek out and use all knowledge relevant to the problem, including knowledge of methods validated for the situation. Twenty-eight guidelines are logically deduced from the Golden Rule. A review of evidence identified 105 papers with experimental comparisons; 102 support the guidelines. Ignoring a single guideline increased forecast error by more than two-fifths on average. Ignoring the Golden Rule is likely to harm accuracy most when the situation is uncertain and complex, and when bias is likely. Non-experts who use 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 evidence-based forecasting methods, forecasting practice in many fields has failed to improve over the past half-century.