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We summarize the literature on the effectiveness of combining forecasts by assessing the conditions under which combining is most valuable. Using data on the six US presidential elections from 1992 to 2012, we report the reductions in error obtained by averaging forecasts within and across four election forecasting methods: poll projections, expert judgment, quantitative models, and the Iowa Electronic Markets. Across the six elections, the resulting combined forecasts were more accurate than any individual component method, on average. The gains in accuracy from combining increased with the numbers of forecasts used, especially when these forecasts were based on different methods and different data, and in situations involving high levels of uncertainty. Such combining yielded error reductions of between 16% and 59%, compared to the average errors of the individual forecasts. This improvement is substantially greater than the 12% reduction in error that had been reported previously for combining forecasts.
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Combining Forecasts: An Application to Elections
Andreas Graefe
Department of Communication Science and Media Research
LMU Munich, Germany
J. Scott Armstrong
Wharton School
University of Pennsylvania, Philadelphia, PA, USA
Randall J. Jones
Department of Political Science
University of Central Oklahoma, Edmond, OK, USA
Alfred G. Cuzan
Department of Government
University of West Florida, Pensacola, FL, USA
January 28th, 2013
Abstract. We summarize the literature on the effectiveness of combining forecasts by assessing
the conditions under which combining is most valuable. Using data on the six U.S. Presidential elections
from 1992 through 2012, we then report the reduction in error obtained by averaging forecasts within and
across four election forecasting methods: poll projections, expert judgment, quantitative models, and the
Iowa Electronic Markets. Across the six elections, the resulting combined forecasts were on average more
accurate than each of the component methods. The gains in accuracy from combining increased with the
number of forecasts used, especially when these forecasts were based on different methods and different
data, and in situations involving high uncertainty. Combining yielded error reductions ranging from 16%
to 59%, compared to the average errors of the individual forecasts. This improvement is substantially
greater than the 12% reduction in error that had been previously reported for combining forecasts.
Electronic copy available at:
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Combining has a rich history, not only in forecasting. In 1818, Laplace wrote, “in combining the results of
these two methods, one can obtain a result whose probability law of error will be more rapidly decreasing
(as cited in Clemen, 1989). In using photographic equipment to combine portraits of people, Galton (1879,
135) found that “all composites are better looking than their components, because the averaged portrait of
many persons is free from the irregularities that variously blemish the look of each of them.In the field of
population biology, Levins (1966) noted that, rather than striving for one master model, it is often better to
build several simple models that, among them, use all the information available, and then average them.
Zajonc (1962) summarizes related literature in psychology, which dates from the early 1900s. Note that
these early applications of combining related to estimation problems, rather than forecasting.
In more recent years, researchers have adopted combining as a simple and useful approach to
reduce forecast error. Armstrong (2001) reviewed the literature to provide an assessment of the gains in
accuracy that can be achieved by combining two or more numerical forecasts. Across thirty studies, the
average forecast had 12% less error than the typical forecasts. In addition, the combined forecasts were
often more accurate than the most accurate component forecast.
One intuitive explanation as to why combining improves accuracy is that it enables forecasters to
use more information and to do so in an objective manner. Moreover, bias exists in the selection of data
and in the forecasting methods that are used. Often the bias is unique to the data and to the method, so that
when various methods using different data are combined in making a forecast, bias tends to cancel out in
the aggregate.
Research interest in combining forecasts has increased since publication of a frequently-cited
paper by Bates and Granger (1969). Numerous studies have demonstrated the value of combining and
tested many proposed methods of weighting the components (for example, based on their historical
accuracy), rather than using simple equal-weight averages. However, in an early review of more than two
hundred published papers, Clemen (1989) concluded that using equal weights provides a benchmark that is
difficult to beat by more sophisticated approaches.
In 2004, we started the project to test the benefits of combining forecasts of U.S.
presidential elections. Forecasts for predicting election outcomes, produced by the following methods,
were collected and processed: polls, prediction markets, experts’ judgment, and quantitative models. We
expected large gains in forecast accuracy, since forecasts using such diverse methods and data provided
ideal conditions for combining (Armstrong, 2001). We had no strong prior evidence as to the relative
performance of each method. For this reason, we decided to combine the forecasts using equal weights.
This approach provided additional benefits, including simplicity of calculation and the resulting potential
appeal to a broad audience.
Electronic copy available at:
In the following sections, we briefly discuss why and how combining works, and outline the
conditions under which it is most useful. We then report results from combining forecasts for six U.S.
presidential elections, three of which were predicted ex ante. The results reveal that combining forecasts
under ideal conditions yields large gains in accuracy, much larger than previously estimated by Armstrong
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In this section we explain the terms used to describe the mechanism of combining that was
employed in this study, which was to calculate simple averages of forecasts.
The error that is derived by averaging the absolute deviations of a set of N numerical forecasts Fi
from the actual value A is termed the "typical error":
The typical error is thus the error that one can expect by randomly selecting an individual forecast
from a given set of forecasts. In mathematical terms, it is similar to the expected value.
By comparison, the “combined error” is the error that is determined by first averaging the N
forecasts Fi, and then comparing that average with the outcome A:
When one forecast is higher than the actual score that was predicted, and one is lower,
"bracketing" occurs (Larrick & Soll 2006). That is, the value to be predicted lies within the range of a set
of forecasts. In this situation, the combined error will invariably be lower than the typical error. When
bracketing does not exist, the typical error and the combined error will be of the same magnitude. In that
case combining will not improve accuracy, but it will not diminish accuracy either.1
In the 2012 election, President Obama won 52.0% of the two-party popular vote. Several months
1 Note that the benefits of combining are limited to numerical forecasts and do not apply to categorical data. The reason is that for categorical
forecasts, bracketing is not possible. In such cases, combining can harm forecast accuracy (see Armstrong et al., 2013).
before the election Abramowitz’s “time for change” model (2012) predicted that Obama would receive
50.6% of the two-party vote for president, which was 1.4 percentage points lower than the result. Near the
same time, the model by Klarner (2012) predicted that Obama would garner 51.2% of the vote, which was
0.8 percentage points too low. Since both models under-predicted the outcome, no bracketing occurred;
hence, the typical error was equal to the combined error: 1.1 percentage points. That is, combining did as
well as randomly picking one of the forecasts. In addition, combining did avoid the risk of picking the
forecast model that incurred the largest error. However, combining also prevented one from picking the
most accurate forecast.2
Now, consider a situation in which two forecasts lie on either side of the true value, bracketing it.
The 2012 forecast of the Erikson & Wlezien model (2012a) was 52.6%. Thus, the typical error of the two
models by Abramowitz and Erikson & Wlezien was 1.0 percentage points. However, the average of the
two forecasts (51.6%) missed the true value by only 0.4 percentage points. In this situation, combining the
forecasts of both models reduced the error of the typical individual model by 60%. In addition, the
combined forecast was more accurate than each of the individual forecasts.
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Combining is applicable to many estimation and forecasting problems. The only exception is when
strong prior evidence exists that one method is best and the likelihood of bracketing is very low.
Armstrong (2001) proposed ex ante conditions under which the gains in accuracy that result from
combining are expected to be highest: (1) a number of evidence-based forecasts can be obtained; (2) the
forecasts draw upon different methods and data; and (3) there is uncertainty about which forecast is most
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Accuracy gains that result from combining are most likely to occur when forecasts from many
evidence-based methods are combined. By “evidence-based forecasts, we mean forecasts that are
generated using methods that adhere to accepted forecasting procedures for the given situation. (A useful
tool in making this assessment is the Forecasting Audit at
When combining, Armstrong (2001) recommended using at least five forecasts. Adding more
forecasts may improve accuracy, though at a diminishing rate of improvement. Nine of the thirty studies in
his meta-analysis were based on combining forecasts from two methods; four of these studies used
forecasts from the same method. None of the studies combined forecasts from four or more different
2 In most real-world forecasting situations, however, it is difficult to identify the most accurate forecast among a set of forecasts (see Section 3.3).
methods. Vul and Pashler (2008) plotted the errors for combinations of a varying number of estimates. The
size of the error shrank as more estimates were included in the combination, although, again, at a
diminishing rate. Jose and Winkler (2008) provided similar results for combinations of five, seven, and
nine forecasts.
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Combining forecasts is most valuable when the individual forecasts are diverse in methods used
and in the theories and data upon which they are based. The reason is that such a set of forecasts is likely
to include different biases and random errors and, thus, should lead to bracketing and low correlations of
Batchelor and Dua (1995) analyzed combinations of 22 U.S. economic forecasts that differed in
their underlying theories (e.g., Keynesian, Monetarism, or Supply Side) and methods (e.g., judgment,
econometric modeling, or time-series analysis). The authors found that the larger the differences in the
underlying theories or methods of the component forecasts, the higher the extent and probability of error
reduction through combining. For example, when combining real GNP forecasts of two forecasters,
combining the five percent of forecasts that were most similar in their underlying theory reduced the error
of the typical forecast by 11%. By comparison, combining the five percent of forecasts that were most
diverse in their underlying theory yielded an error reduction of 23%. Similar effects were obtained
regarding the underlying forecasting methods. Error reduction from combining the forecasts derived from
the most similar methods was 2%, compared to 21% for combinations of forecasts derived from the most
diverse methods.
Winkler and Clemen (2004) reached a similar conclusion. In their laboratory experiment, they
asked each participant to use six different strategies for generating six different solutions to an estimation
task. Then, the authors analyzed the relative accuracy of different combining approaches. The results
showed that combining estimates across participants was generally more accurate than combining different
estimates by the same participant. On average, combining a single estimate from two participants was
more accurate than combining four estimates from the same participant.
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Rather than combine forecasts, some analysts argue that it is better to simply pick the most
accurate forecast. This objection seems to be of little practical relevance. Although a method’s past
performance may be an indication of its future performance, there is no assurance that the method will
continue to be as accurate as in the past.3 Under such uncertainty, there is little likelihood that one will be
able to determine which method will be most accurate in the future.
A study that was conducted to examine the strategies people use to make decisions based upon two
sources of advice provided experimental evidence: instead of combining the advice, the majority of
participants tried to identify the most accurate source and thereby reduced accuracy (Soll & Larrick,
2009). In most real-world forecasting situations, there is no assurance beforehand that the selected forecast
will be the most accurate. As a result, when picking a single forecast, one takes the risk of choosing a poor
forecast. The prudent forecaster, therefore, may want to minimize this risk by combining, even though a
particular forecast could eventually prove to be more accurate than the combination.
Research by Hibon and Evgeniou (2005) supports this approach. The authors compared the
relative risk associated with two strategies for predicting the 3,003 time series used in the M3-competition
based on forecasts from fourteen methods: choosing an individual forecast or relying on various
combinations of forecasts. Risk was measured as the incremental error that resulted from failing to identify
the best individual forecast. When compared to randomly picking an individual forecast, choosing a
random combination of all possible combination forecasts reduced risk by 56%.
Turning to the opposing argument, assume that the forecaster does have very good evidence that a
given forecast method will be the most accurate. Even in this situation, combining, nevertheless, may
improve accuracy. Herzog and Hertwig (2009) and Soll and Larrick (2009) illustrate when combining is
better than picking a single forecast, even when one has complete knowledge about which individual
forecast is the most accurate. For example, the average of two forecasts is more accurate than the best
individual forecast if two conditions are met: (1) the two forecasts bracket the actual score being predicted,
and (2) the absolute error of the less accurate forecast does not exceed three times the absolute error of the
most accurate forecast.
As noted previously, Clemen (1989) reviewed the literature on combining forecasts and concluded
that equally weighting the individual forecasts is often the best course of action when combining. More
than twenty years later, these results still remain valid.
In a recent study Genre et al. (2013) analyzed various sophisticated approaches to combining
forecasts from the European Central Bank’s Survey of Professional Forecasters. Although at times some of
3 Election forecasting is no exception. Holbrook (2010) analyzed the relative accuracy of nine established econometric models for the elections
from 1996 to 2004. He found that the models’ accuracy varied considerably within and across elections and that there was no single model that
was always the most accurate.
the complex combining methods outperformed the simple averages, no approach was consistently more
accurate over time, across target variables, and across time horizons. Stock and Watson (2004) arrived at
similar results when analyzing the relative performance of several combining procedures for economic
forecasts, using a seven-country data set over the time period from 1959 to 1999. Sophisticated
combination methods, which relied heavily on historical performance for weighing the component
forecasts, performed worse than a simple average of all available forecasts.
Stock and Watson have coined the term “forecast combination puzzle” when referring to the
repeated empirical finding that the simple average often outperforms more complex approaches (p.428).
The authors explained their results as a consequence of the instability of individual forecasts, since the
performance of individual forecasts varied widely over time, depending on external effects such as
economic shocks or political factors. In other words, good performance in one year or country did not
predict good performance in another, which limits the value of differential weights (see also Section 3.3).
Smith and Wallis (2009) provided a formal explanation for the forecast combination puzzle,
showing that the reason is estimation error. Based on results from a Monte Carlo study of combinations of
two forecasts, and a reappraisal of a published study on different combinations of multiple forecasts of US
output growth, they found that a simple average of forecasts is expected to be more accurate than estimated
optimal weights if (a) the optimal weights are close to equality and if (b) a large number of forecasts are
combined. The reason is that, in such a situation, each forecast has a small weight, and the simple average
provides an efficient trade-off against the error that arises from the estimation of weights.4
In summary, a large body of analytical and empirical evidence supports the use of equal weights
when combining forecasts. In addition to their accuracy, simple averages have another major benefit: they
are easy to describe, understand, and implement.
This is not to say that equal weights will always provide the best results. For example, estimated
weights might be useful if one faces a limited number of forecasts that differ widely in accuracy, and one
can rely on a large sample that allows for estimating robust weights. In addition, there are useful and
accessible alternatives to simple averages that do not require estimating weights, such as trimmed and
Winsorized means. These measures eliminate the most extreme data points when calculating averages and
thus can provide more robust estimates than the simple average. Jose and Winkler (2008) analyzed the
relative performance of simple averages, trimmed, and Winsorized means for using datasets from the M3
Competition and the Survey of Professional Forecasters of the Federal Reserve Bank of Philadelphia. The
4 These results conform to a large body of evidence on the use of weights in linear models. These studies found the relative performance of unit (or
equal) weights compared to differential weights increases with small samples, a large number of predictor variables, and high correlation among
predictor variables (Dawes, 1979; Einhorn & Hogarth, 1974; Graefe & Armstrong, 2011).
authors found that trimmed and Winsorized means were slightly more accurate than the simple average, in
particular when there was large variability among the individual forecasts. In general, the research
available suggests that the performance of different combination methods depends on the conditions faced
by the forecaster. Forecasters might want to use different rules for combining, depending on the conditions
of the forecasting problem (Collopy & Armstrong, 1992).
Regardless of the selected combining approach, a general rule is to specify the procedure for how
to combine prior to analyzing the data, as this ensures objectivity. Without prior specification, the
combined forecasts can be manipulated for political purposes or simply to make them fit with what the
forecaster might desire, an effect that might not even be apparent to the forecaster.
In this section we combine forecasts of the two-party popular vote shares in U.S. presidential
elections. Several valid methods are commonly used to predict election outcomes. These include polls,
experts’ judgment, quantitative models, and prediction markets. Each of these methods uses a different
approach and draws upon data from different and varied sources. Election forecasts using these methods,
therefore, are well suited for assessing the value of combining. The analysis includes the six elections from
1992 to 2012.
Our approach to combining presidential election forecasts was to weight all component methods
equally. Given the importance of combining across methods, we first combined within and then across
component methods. In other words, we used equal weighting of all forecasts within each component
method, then equal weighting across forecasts from different methods. The rationale behind choosing this
procedure was to equalize the impact of each component method, regardless of whether a component
included many forecasts or only a few. For example, while only one suitable prediction market was
available, there were forecasts from several quantitative models that used a similar method and similar
information. In such a situation, a simple average of all available forecasts would over-represent models
and under-represent prediction markets, which we expected would harm the accuracy of the combined
We do not suggest that this approach will generate optimal forecasts, nor do we attempt to
include all available forecasts. We describe the general procedure that was used, which was guided by the
recommended principle to define the combining procedures a priori (Armstrong, 2001).5 We provide full
5 For the past three elections in from 2004 and to 2012, we provided ex ante forecasts, which were continuously updated throughout the campaigns
disclosure of our data in the hope that other researchers will build upon our work. All data will be made
publicly available at the IJF website.6
In the following subsections we describe the four forecasting methods that were used in this
analysis, and explain our approach to combining forecasts within each method. Predictions from polls,
models, and the Iowa Electronic Markets (IEM) were available for all six elections in our study, 1992 to
2012. In addition, we conducted our own expert surveys for the three elections from 2004 to 2012. The
results of combining forecasts from these methods will be presented in Section 5.2.$Polls$
Campaign or trial heat” – polls reveal voter support for candidates in an election campaign.
Typically, voters are asked which candidate they would support if the election were held today. Thus, polls
do not provide predictions but rather are snapshots of current opinion. Nonetheless, polls are a common
means of forecasting election outcomes. Scholars, the news media, and the public commonly interpret
polls as forecasts and project the results to Election Day.
Campbell and Wink (1990) analyzed the accuracy of Gallup trial heat polls for the eleven
presidential elections from 1948 to 1988. The use of raw polls to forecast presidential elections produced
large errors, which were greater as the time before the election was longer. Other research has shown that
polls conducted by reputable survey organizations at about the same time often reveal considerable
variation in results. Errors caused by sampling problems, non-responses, inaccurate measurement, and
faulty processing diminish the accuracy of polls and the quality of surveys more generally (e.g., Erikson &
Wlezien, 1999; Wlezien, 2003).
A simple approach to increasing poll accuracy is to combine polls that are conducted by different
organizations near the same time. Using the median of all state-level polls taken within a month of the
presidential election, Gott and Colley (2008) correctly predicted Bush's victory over Kerry in 2004 with an
error of only four electoral votes. They also forecast Obama to win over McCain in 2008 with an error of
only two electoral votes. In both elections, the median statistics approach missed the winner in only one
and posted at In the present study, we report all forecasts as if they were calculated ex post. As a result, the combining
procedure described here may slightly differ from the calculation of ex ante forecasts that was actually performed in these elections. However, for
reasons of simplification and consistency, the present manuscript describes an identical approach to combining across all elections. The actual
specifications of the PollyVote in each of these years are described in recap pieces of each election, which were published in Foresight The
International Journal of Applied Forecasting (Cuzán et al., 2005; Graefe et al., 2009, 2013).
6 For now, the links to the data files can be accessed at:
state. Simply aggregating polls has also become popular in the news media. Well-known poll aggregators
such as and the Huffington Post Pollster update combined polls on an almost daily
A more sophisticated approach to increasing poll accuracy is to calculate "poll projections", as we
term them. Poll projections take into account the historical record of the polls when making predictions of
the election outcome. For example, assume that the incumbent leads the polls by 20 points in July. In
analyzing historical polls conducted around the same time along with the respective election outcomes,
one can derive a formula for translating the July polling figures into an estimate of the incumbent’s
expected final vote share. This is commonly done by regressing the incumbent’s share of the vote on his
polling results during certain time periods before the election. Prior research has found that such poll
projections are much more accurate than treating raw polls as forecasts (Campbell & Wink 1990;
Campbell 1996; Erikson & Wlezien 2008).
In the present study, we adopted an approach for combining and damping polls that is similar to
Erikson and Wlezien (2008). For each of the 100 days prior to a presidential election, starting with 1952,
we averaged the incumbent party candidate's two-party support from all polls that were released over the
previous seven days. When no polls were released on a given day, the most recent poll average available
was used. Then, for each of the 100 days before the election, we regressed the incumbent’s actual two-
party share of the popular vote on the poll value for that day. This process produced 100 vote equations
(and thus poll projections) per election year. Successive updating was used to calculate ex ante poll
projections. That is, when generating poll projections of the 1992 election, only historical data from the
elections from 1952 to 1988 were used. When calculating poll projections of the 2012 election, all polls
through 2008 were used. Polling data were obtained from the iPoll databank of the Roper Center for
Public Opinion Research.$$Experts$
Before the emergence of polls in the 1930s, judgments from political insiders and experienced
observers were commonly used for forecasting (Kernell, 2000). They still are. Expert analysts are assumed
to be independent when making predictions, and they have experience in reading and interpreting polls,
assessing their significance during campaigns, and estimating the effects of recent or expected events on
their results.
Experts can be expected to use different approaches and rely on various data sources when
generating forecasts. Thus, combining experts’ judgments should increase forecast accuracy. We were
unable to find prior studies on the gains from combining expert forecasts of election results. However, we
did locate two expert surveys that were conducted shortly before the 1992 and 2000 U.S. presidential
elections, from which we re-calculated the gains from combining the individual predictions. In 1992, the
average forecast of ten expert predictions was 4% more accurate than the forecast of the typical individual
expert.7 In 2000, the average forecast was 72% more accurate than the typical forecast from fifteen
For the three elections from 2004 to 2012, we formed a panel of experts and contacted them
periodically for their estimates of the incumbent’s share of the two-party popular vote on Election Day.
Most experts were academic specialists in elections, though a few were analysts at think tanks,
commentators in the news media, or former politicos. We deliberately excluded election forecasters who
developed their own models, because that method was represented as a separate component in our
combined forecast (see Section The number of respondents in each of the three surveys
conducted in 2004 ranged from twelve to sixteen. For the four surveys in 2008, the number of respondents
ranged from ten to thirteen. For the eleven surveys conducted in 2012, the number of respondents ranged
from twelve to sixteen. Our combined expert forecast was the simple average of forecasts made by the
individual experts.9 Because our panelists did not meet in person, the possibility of bias due to the
influence of strong personalities or individual status was eliminated.$$Quantitative$models$
A common explanation of electoral behavior is that elections are referenda on the incumbent
party’s performance during the term that is ending. For more than three decades, scholars have amplified
and tested this theory, most commonly by developing econometric models, usually to predict the outcome
of U.S. presidential elections. Most models include two to five variables and typically combine indicators
of economic conditions and public opinion to measure the incumbent’s performance. For example, models
by Abramowitz (2012), Campbell (2012), Lewis-Beck and Tien (2012), and Erikson and Wlezien (2012a)
all include a variable measuring opinion (presidential approval or support for the incumbent candidate)
along with economic data. For descriptions of early election forecasting models (and other methods), see
Lewis-Beck and Rice (1992), Campbell and Garand (2000), and Jones (2002). For overviews of the
variables used in the most popular models see Jones and Cuzán (2008) and Holbrook (2010).
Since the 1990s, forecasts of competing models have been regularly published near Labor Day of
the election year. For the past five elections, the forecasts of leading models were published in American
7 The Washington Post. Pundits’ brew: How it looks; Who’ll win? Our fearless oracles speak, November 1, 1992, p. C1, by David S. Broder.
8 The Hotline. Predictions: Potpourri of picks from pundits to professors, November 6, 2000.
9 In 2004, we used the Delphi survey method, though from 2008 on we eliminated the feedback step and the opportunity to modify initial
estimates, since the experts rarely changed their first estimates.
Politics Research, 24(4) and PS: Political Science and Politics, 34(1), 37(4), 41(4) and 45(4). Most models
predicting presidential elections have produced forecasts using data available near the end of July in the
election year. Usually models have correctly predicted the election winner, albeit by varying accuracy as to
candidates' vote shares. Forecast errors for a single model can vary widely across elections, and the
structure of some of the models has changed over time, so it is difficult to identify the most accurate
Prior research demonstrated that combining predictions from election forecasting models is
beneficial to forecast accuracy. Bartels and Zaller (2001) used various combinations of structural variables
that are included in prominent presidential election models to construct 48 different models. The variables
included six indicators of economic performance, a measure of the relative ideological moderation of the
candidates, a measure for how long the incumbent party has held the White House, and a dummy for war
years. We re-calculated the typical error of the 48 models for predicting the 2000 election from their data
(Bartels & Zaller, 2000, Table 1), which was 3.0 percentage points. By comparison, the combined error for
all models was 2.5 percentage points. That is, combining reduced the error of the typical model by 17%. In
a response to Bartels and Zaller, Erikson et al. (2001) showed that creating models that combine structural
variables with public opinion further increases accuracy. The authors added presidential approval as an
additional variable to the 48 models, thus doubling the number of models to 96. The sum of the absolute
errors for their averaged models was 32% lower than for the averaged Bartels and Zaller models.
Montgomery et al. (2012) combined the forecasts from six established econometric models based
on their past performance and uniqueness, using an approach called Ensemble Bayesian Model Averaging
(EBMA). Across the nine elections from 1976 to 2008, the error of the combined EBMA forecast was 34%
lower than the error of a typical individual model. However, as shown by Graefe (2013), the error of the
EBMA forecast was 18% higher than the error of the simple average.
In the present study, we used forecasts from six models in 1992, eight in 1996, nine in 2000, ten in
2004, sixteen in 2008, and twenty-two in 2012. As noted, forecasts for most models were released by late
July, and some were updated once, or more often, as revised data became available. Whenever changes
occurred, we recalculated the model averages. All of the models were developed by academics and either
published in academic journals or presented at academic conferences.10
10 Model forecasts by Abramowitz (2012), Campbell (2012), Fair (2009), and Erikson & Wlezien (2012a) were available for all six elections.
Forecasts by Holbrook (2012), Lewis-Beck and Tien (2012), Lockerbie (2012), and Norpoth & Bednarczuk (2012) were available for the five
elections from 1996 to 2012. Forecasts by Cuzán (2012) were available for the four elections from 2000 to 2012. Forecasts by Hibbs (2012) were
available for the three elections from 2004 to 2012. Forecasts by Lichtman (2008), Graefe and Armstrong (2012), Jerôme & Jerôme-Speziari
(2012), DeSart and Holbrook (2003), and Klarner (2008) were available for 2008 and 2012. A forecast by Lewis-Beck and Rice (1992) and
Sigelman (1994) was available for the 1992 election. A forecast by Haynes and Stone (2008) was available for the 2008 election. A forecasts by
Betting on election outcomes has a long history, and has been recognized as a useful means of forecasting
election outcomes. Rhode and Strumpf (2004) studied historical markets that existed for the fifteen
presidential elections from 1884 through 1940 and concluded that these markets “did a remarkable job
forecasting elections in an era before scientific polling” (p.127).
These markets were the precursors of today's online prediction markets, the oldest being the Iowa
Electronic Markets (IEM), which were established at the University of Iowa in 1988. In this study we used
prices from the IEM vote-share market as predictions of the vote. In comparing forecasts from the IEM
with 964 polls for the five presidential elections from 1988 to 2004, Berg et al. (2008) determined that
74% of the time the IEM forecasts were closer to the actual election result than polls conducted on the
same day. However, Erikson and Wlezien (2008) found poll projections to be more accurate than IEM
Prediction market forecasts can be negatively affected by unexpected spikes in prices due to
information cascades, which occur when people buy or sell shares simply because of the observed actions
of other market participants (Anderson & Holt, 1997). We expected that combining market forecasts over
a given time period could moderate these short-term disruptions in market prices. We thus combined IEM
forecasts by calculating the 7-day rolling average of daily prices of the vote-share contract for the
incumbent party candidate. The effect on forecast accuracy of combining IEM prices was determined by
comparing the 7-day average to the daily IEM average.
Although some previous research has assessed the value of combining election forecasts within
methods (e.g., Montgomery et al., 2012), we are not aware of any prior research that has combined
forecasts both within and across methods, which is the approach presented here. Each of the four
component methods in our study could be expected to produce valid forecasts, but we anticipated that the
most significant gains in accuracy would come from combining across the methods. This is because the
four methods differ in technique and assumptions, in the types of data used, and in data sources. We
recognized that the demonstrated accuracy of the IEM and poll projections might diminish the gains from
combining across methods. We also were aware that the impact of a dominant method tends to fade as the
number of component methods increases.
For each day in the forecast horizon, we calculated a simple average across the combined
Armstrong & Graefe (2011), Campbell’s (2012) convention bump model, Berry & Bickers (2012), Graefe (2012), Graefe & Armstrong (2012),
Lewis-Beck and Rice’s (2012) proxy model, and Nate Silver’s was available for the 2012 election.
component forecasts: poll projections, experts, models, and IEM. We refer to this overall combined
forecast as the PollyVote.11
All of the reported forecasts refer to the two-party popular vote share of the candidate of the
incumbent party. All analyses are conducted across the last 100 days prior to Election Day. That is, for the
six elections from 1992 to 2012, we calculated daily forecasts and the corresponding errors for each of the
100 days prior to Election Day. Thus, we obtained 600 daily forecasts from polls, models, and the IEM.
Our own expert forecasts were available only for the three elections in 2004, 2008, and 2012, for a total of
296 daily forecasts.12
We used the absolute error as a measure of accuracy (that is, the difference between the predicted
and actual vote shares, regardless whether the error was positive or negative). In presenting the gains
achieved through combining, we report the "error reduction" in percent. By this we mean the extent to
which the combined error is smaller than the typical error of a set of forecasts:
AEtypical AEcombined
For example, the combined error of the 2012 election forecasts by Abramowitz (2012) and
Erikson & Wlezien (2012a) was 0.4 percentage points, compared to 1.0 percentage points for the typical
error (see Section 2.2). Thus, the error reduction derived through combining was 60%. When analyzing
accuracy across time periods such as days or years, we report mean error reduction (MER). The MER for a
particular election year is determined by averaging the typical and combined errors across the 100-day
time-period before calculating the error reduction. The MER across years is the simple average of the error
reduction of each particular year.13
In Table 1 the section labeled "within component combining" shows the MER over the 100-day
forecast horizon that is achieved by combining forecasts within a method category. On average across the
six elections, combining poll projections yielded the largest error reductions (39%), even though the
11 PollyVote stands for “many” and “politics.” On our website, we playfully adopted a parrot as a mascot because the method does little else than
repeat and combine what it borrows (or “hears”) from others.
12 In 2004, the first expert forecast was not available before 96 days prior to Election Day.
13 We report only effect sizes and avoid statistical significance. For an explanation, see Armstrong (2007).
approach produced less accurate forecasts than individual polls in 2008.14 Error reductions were also
substantial when combining within the remaining methods: models (30%), expert forecasts (12%), and the
IEM (10%). Calculating 7-day averages of IEM prices resulted in more accurate forecasts than the original
IEM in each election year except for 1992.
The "across component combining” section of Table 1 shows the MER of the PollyVote forecast
compared to the error of the combined forecasts of component methods. Across the six elections, the
PollyVote provided more accurate forecasts than each of its components. On average, the PollyVote
forecast was 49% more accurate than the combined experts, 34% more accurate than the combined
models, 27% more accurate than the poll projections, and 7% more accurate than the IEM 7-day average.
Within component combining
Poll projections vs. typical poll
Model average vs. typical model
Combined experts vs. typical expert
7-day IEM average vs. original IEM
Across components combining: PollyVote vs.
Poll projections
Model average
IEM (7-day average)
Within and across combining: PollyVote vs.
Typical individual poll
Typical individual model
Typical individual expert
Original IEM
The section of Table 1 labeled within and across combining” shows the MER of the PollyVote
forecast compared to the typical (uncombined) forecasts of each component method. Gains in accuracy
14 The poor performance of poll projections in 2008 can likely be attributed to the economic crisis that hit in mid-September of that year, less than
two months before Election Day. With this event, the gap in the polls increased decisively in favor of Obama, an effect that was detrimental to the
accuracy of the damped poll projections. See Campbell (2010) for a discussion of the decisive impact of the economic crisis on the 2008 election
were large compared to the typical individual poll (59%), the typical model (58%), and the typical expert
(55%). In each case, combining reduced error by more than half. Compared to the original IEM, the
PollyVote reduced the error by 16% on average, which is higher than Armstrong’s (2001) earlier estimate
of the benefits of combining of 12%.15
Table 2 shows the percentage of days in which bracketing occurred and the MER compared to the
typical component method for the each of the three elections from 2004 to 2012.16 As expected, the percent
of days with bracketing rose with the number of components included in the forecast.$$Combinations$of$two$component$methods$
On average, combining across two methods led to a 23% error reduction relative to the typical
component forecast. Combinations of IEM and expert forecasts yielded the largest gains in accuracy (error
reduction: 29%). On the other hand, gains from combining models and poll projections were smallest
(17%). A possible reason for the low rate of bracketing for models and poll projections might be that many
models already include information from polls to measure public opinion. In contrast, models are limited
when it comes to incorporating information about the specific context of a particular election; this might be
the reason why high rates of bracketing occur when combining models with methods that incorporate
human judgment, such as expert forecasts or the IEM. Gains in accuracy were also relatively small when
combining poll projections and the IEM forecasts. This conforms to results by Erikson and Wlezien
(2012b), who showed that prediction market forecasts mostly follow the polls.$$Combinations$of$three$component$methods$
On average, the combinations of three components led to error reductions of 37% relative to the
typical forecast. Error reductions were largest if the model forecasts were combined with human judgment
from experts and the IEM (48%). The error reductions were smallest although still at the substantial level
of 31% for the combination of models, polls, and the IEM.
15 The "hit rate" provides additional insight on the relative accuracy of the PollyVote and the IEM. Hit rate refers to the frequency with which
forecasts of a given method correctly predict the popular vote winner, expressed as a percent of all available forecasts of that method. The hit rate
thus measures a method’s capability to answer the question that is probably most interesting to the regular consumer of election forecasts: who will
win (rather than what will a candidate’s share of the vote be)? Based on the hit rate the PollyVote outperformed the original IEM in four of the six
elections, with two ties. On average, the PollyVote predicted the correct election winner on 97% of all 600 days in the forecast horizon, compared
to a hit rate of 80% for the IEM.
16 The reason for limiting this analysis to only three elections is that only for these elections, forecasts from all four component methods were
Combinations based on
% of days with
MER to typical
(in %)
Two component methods
IEM & experts
Models & IEM
Poll projections & experts
Poll projections & IEM
Models & experts
Models & poll projections
Three component methods
Models & IEM & experts
Poll projections & IEM & experts
Models & poll projections & experts
Models & poll projections & IEM
Four component methods
The combination of four methods led to an error reduction of 48% relative to the typical forecast.
In nearly three out of four cases (72%), combining the forecasts from all four component methods
produced bracketing.
There are many reasons for uncertainty in forecasting, such as high disagreement among forecasts
or long lead times. In the following discussion, we analyze the benefits of combining under these
If forecasts derived from different methods agree, certainty about the situation usually increases.
In contrast, high disagreement among forecasts indicates high uncertainty. Disagreement among forecasts
is often used as a conservative ex ante measure for uncertainty. For example, in analyzing 2,787
observations for inflation and 2,342 observations for GDP forecasts from the Survey of Professional
Forecasters, Lahiri and Sheng (2010) confirmed evidence from earlier research showing that disagreement
within a given method tends to underestimate the level of uncertainty.
Table 3 shows the MER of the PollyVote compared to the typical component for different levels of
uncertainty, calculated across all 600 days in the dataset. Uncertainty was measured as the range between
the highest and lowest component forecast at any given day. For example, a situation in which the lowest
component forecast predicts the incumbent to gain 50% of the vote, and the highest component forecast
predicts him to gain 52%, would represent a range of two percentage points. As shown in Table 3, on
nearly half of all (285 out of 600) days, the range between the component forecasts was within two to four
percentage points. In these situations, the PollyVote reduced the error of the typical forecast by about 43%.
In general, the MER of the PollyVote compared to the typical component increased as uncertainty
increased. That is, the benefits from combining were larger when disagreement among component
forecasts, and in effect the chance of bracketing, was higher.
ER in %
Uncertainty usually increases with the time horizon of the forecast. Accordingly, combining
should be more helpful early in a campaign. Figure 1 shows the MER, calculated across all six elections,
of the combined PollyVote forecast compared to the forecast of the typical component for the last 100 days
prior to Election Day.
As expected, the gains from combining are high early in the campaign, with a mean error
reduction of nearly 1.5 percentage points. Subsequently, the gains from combining decrease as the election
nears, which suggests that the forecasts from the different components tend to converge as uncertainty
decreases. Interestingly, the gains from combining increase again in the period from one month to two
weeks before Election Day, which is about the time when the presidential debates are usually held. It is up
to future research to clarify what is going on late in the campaign, for example, whether the results are
driven by a particular forecasting component.
In applying a two-step approach of combining forecasts within and across four methods for
forecasting U.S. presidential elections, we achieved large gains in accuracy. Compared to forecasts from a
randomly chosen poll, model, or expert, the PollyVote forecast reduced error by 55% to 59%. Compared to
the original IEM, essentially a sophisticated approach for aggregating and combining dispersed
information, the PollyVote reduced error by 16%. Across the six elections, the PollyVote provided more
accurate forecasts than each of its components. While combining is useful under all conditions, it is
especially valuable in situations involving high uncertainty.
These gains in accuracy were achieved by using equal weights for combining the forecasts. Equal
weights seemed to be an appropriate and pragmatic choice, as there is a lack of prior knowledge on how to
weight the methods, as well as insufficient data to analyze the effects of differential weights. In addition,
equal weights are simple to use and easy to understand. That being said, further improvements might be
possible if additional knowledge is gained about the relative performance of the different methods and
their historical track record under certain conditions, such as their accuracy during different points in time
in an election cycle.
Combining should be applicable to predicting other elections and, more generally, can be applied
in many other contexts, as well. Given the various methods available to forecasters, combining is one of
the most effective and reliable ways to improve forecast accuracy and prevent large errors. Of course, the
gains in accuracy from adding additional methods accrue at a diminishing rate, so there is a point at which
costs exceed benefits.
Over the past half-century, practicing forecasters have advised firms to use combining. For
example, the National Industrial Conference Board (1963) and Wolfe (1966) recommended combined
forecasts. PoKempner and Bailey (1970) claimed that combining was a common practice among business
forecasters. Dalrymple’s (1987) survey on the use of combining for sales forecasting revealed that, of the
134 U.S. companies responding, 20% “usually combined”, 19% “frequently combined,” 29%, ”sometimes
combined,” and 32% “never combined”. We suspect however, that the survey respondents were referring
to informal methods of combining, such as weighting individual forecasts based on unaided judgment.
Such approaches to combining do not conform to the procedures as described in this paper.
We believe that combining, properly defined and implemented, is in little use today. A number of
possible explanations exist for the low usage of formal combining:
Lack of knowledge about the research on combining is likely to be a major barrier to the use of
combining in practice. The benefits of combining are not intuitively obvious, and people are unlikely to
learn this through experience. In a series of experiments with MBA students at INSEAD, a majority of
participants thought that an average of estimates would reflect only average performance (Larrick & Soll
Combining seems too simple. Hogarth (2012) reported results from four case studies showing that
simple models often predict complex problems better than more complex ones. In each case, people had
difficulty accepting the findings from simple models. There is a strong belief that complex models are
necessary to solve complex problems. Similarly, people might perceive the principle of combining as “too
easy to be true”.
Forecasters might seek an extreme forecast in order to gain attention. Batchelor (2007) found
long-term macroeconomic forecasts to be consistently biased as a result of financial, reputational, or
political incentives of the forecasting institutions. Forecasters face a general trade-off between accuracy
and attention. More extreme forecasts usually gain more attention, and the media are more likely to report
Forecasters may think they are already using combining properly. Based on the findings from his
meta-analysis, Armstrong (2001) recommended combining forecasts mechanically, according to a
predetermined procedure. In practice, managers often use unaided judgment to assign differential weights
to individual forecasts. Such an informal approach to combining is likely to be harmful, as managers may
select a forecast that suits their biases.
People mistakenly believe that they can identify the most accurate forecast. Soll and Larrick
(2009) conducted experiments to examine the strategies that people use to make decisions based upon two
sources of advice. Instead of combining the advice, the majority of participants tried to identify the most
accurate source and thereby reduced accuracy.
One goal of the project is to help people to overcome these barriers by using the
high-profile application of forecasting U.S. Presidential Election outcomes to demonstrate the benefits of
combining. Software providers might also contribute by including combining as a default. That is,
software solutions should require users to actively opt out of combining after considering its applicability
to the current situation.
Combining forecasts requires that the procedures be specified and fully disclosed prior to the
preparation of the forecasts. This allows for the use of a variety of information in a way that helps to
control for bias. In short, combining must be objective.
We have estimated the improvement in accuracy that can be achieved by combining U.S.
presidential election forecasts within and across methods. The results are consistent with prior research on
combining but the potential gains are much larger than previously estimated. Under ideal conditions,
forecasting errors can be reduced by more than half. Thus, the simple method of combining is one of the
most useful procedures in a forecaster’s toolkit.
If it is possible to use a number of evidence-based forecasting methods and alternative sources of
data, combining forecasts should be considered for all situations that involve uncertainty. Combining
forecasts was shown to be much more useful as uncertainty increases. For important forecasts, the costs of
combining forecasts are likely to be trivial relevant to the potential gains.
Kesten Green and Stefan Herzog provided helpful comments. We also received suggestions when
presenting earlier versions of the paper at the 2009 International Symposium on Forecasting, the 2010
Bucharest Dialogues on Expert Knowledge, Prediction, Forecasting: A Social Sciences Perspective, and
the 2011 Annual Meeting of the American Political Science Association. We sent drafts of the paper to all
authors whose research was cited on substantive points to ensure that we accurately summarized their
research, and we thank all who replied. Kelsey Matevish and Nathan Fleetwood helped to edit the paper.
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Probabilistic forecasting is receiving growing attention nowadays in a variety of applied fields, including hydrology. Several machine learning concepts and methods are notably relevant to formalizing and optimizing probabilistic forecasting implementations by addressing the relevant challenges. Nonetheless, practically-oriented reviews focusing on such concepts and methods are currently missing from the probabilistic hydrological forecasting literature. This absence holds despite the pronounced intensification in the research efforts for benefitting from machine learning in this same literature, and despite the substantial relevant progress that has recently emerged, especially in the field of probabilistic hydrological post-processing, which traditionally provides the hydrologists with probabilistic hydrological forecasting implementations. Herein, we aim to fill this specific gap. In our review, we emphasize key ideas and information that can lead to effective popularizations of the studied concepts and methods, as such an emphasis can support successful future implementations and further scientific developments in the field. In the same forward-looking direction, we identify open research questions and propose ideas to be explored in the future.
... Ensemble models are another type of advanced ML. Ensemble modeling (Kotu and Deshpande, 2015;Mosavi et al., 2019;Zhang and Ma, 2012) is a process where multiple base models (often called "weak learners") are combined into an ensemble model (or "strong learner") with equal weights (Graefe et al., 2014) or different weights (Delen et al., 2017) to improve prediction. The ensemble model aggregates the prediction of each model and produces a final prediction outcome. ...
Purpose For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of crowdfunding by comparing prediction models. Design/methodology/approach With 1,368 features extracted from 15,195 Kickstarter campaigns in the USA, the authors compare base models such as logistic regression (LR) with tree-based homogeneous ensembles such as eXtreme gradient boosting (XGBoost) and heterogeneous ensembles such as XGBoost + LR. Findings XGBoost shows higher prediction accuracy than LR (82% vs 69%), in contrast to the findings of a previous relevant study. Regarding important content features, humans (e.g. founders) are more important than visual objects (e.g. products). In both spoken and written language, words related to experience (e.g. eat) or perception (e.g. hear) are more important than cognitive (e.g. causation) words. In addition, a focus on the future is more important than a present or past time orientation. Speech aids (see and compare) to complement visual content are also effective and positive tone matters in speech. Research limitations/implications This research makes theoretical contributions by finding more important visuals (human) and language features (experience, perception and future time). Also, in a multimodal context, complementary cues (e.g. speech aids) across different modalities help. Furthermore, the noncontent parts of speech such as positive “tone” or pace of speech are important. Practical implications Founders are encouraged to assess and revise the content of their video or text ads as well as their basic campaign features (e.g. goal, duration and reward) before they launch their campaigns. Next, overly complex ensembles may suffer from overfitting problems. In practice, model validation using unseen data is recommended. Originality/value Rather than reducing the number of content feature dimensions (Kaminski and Hopp, 2020), by enabling advanced prediction models to accommodate many contents features, prediction accuracy rises substantially.
... Sales can be treated as time series. Different time series approaches are described in [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. Machine learning is widely used for forecasting different kinds of time series along with classical statistical methods like ARIMA, Holt-Winters, etc. Moderm deep learning algorithms DeepAR [19,20], N-BEATS [21], Temporal Fusion Transformers [22] show state-of-the-art results for time series forecasting. ...
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The paper describes the deep learning approach for forecasting non-stationary time series with using time trend correction in a neural network model. Along with the layers for predicting sales values, the neural network model includes a subnetwork block for the prediction weight for a time trend term which is added to a predicted sales value. The time trend term is considered as a product of the predicted weight value and normalized time value. The results show that the forecasting accuracy can be essentially improved for non-stationary sales with time trends using the trend correction block in the deep learning model.
... As Table 1 indicates, we rely on multiple modes of collection, namely searching for subjects amongst Facebook users and Amazon Mechanical Turk workers. There is evidence that relying on multiple loci for data collection can lead to more robust estimates of effects of interest [23], due to the differential selection effects canceling out over loci; furthermore, we rely on the well established literature in aggregating election forecasts [24,25], and expect that averaging loci-specific selection effects will improve the mean-squared predictive error of our predictions. The turnout probabilities are estimated from the Indian National Election Study combined with aggregate Lok Sahba election statistics. ...
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Recent technological advances have facilitated the collection of large-scale administrative data and the online surveying of the Indian population. Building on these we propose a strategy for more robust, frequent and transparent projections of the Indian vote during the campaign. We execute a modified MrP model of Indian vote preferences that proposes innovations to each of its three core components: stratification frame, training data, and a learner. For the post-stratification frame we propose a novel Data Integration approach that allows the simultaneous estimation of counts from multiple complementary sources, such as census tables and auxiliary surveys. For the training data we assemble panels of respondents from two unorthodox online populations: Amazon Mechanical Turks workers and Facebook users. And as a modeling tool, we replace the Bayesian multilevel regression learner with Random Forests. Our 2019 pre-election forecasts for the two largest Lok Sahba coalitions were very close to actual outcomes: we predicted 41.8% for the NDA, against an observed value of 45.0% and 30.8% for the UPA against an observed vote share of just under 31.3%. Our uniform-swing seat projection outperforms other pollsters—we had the lowest absolute error of 89 seats (along with a poll from ‘Jan Ki Baat’); the lowest error on the NDA-UPA lead (a mere 8 seats), and we are the only pollster that can capture real-time preference shifts due to salient campaign events.
Election forecasting has been traditionally dominated by subjective surveys and polls or methods centered upon them. We have developed a novel platform for forecasting elections based on agent-based modeling (ABM), which is entirely independent from surveys and polls. The platform uses statistical results from objective data along with simulation models to capture how voters have voted in past elections and how they are likely to vote in an upcoming election. We screen for models that can reproduce results that are very close to the actual results of historical elections and then deploy these selected models to forecast an upcoming election with simulations by combining extrapolated data from historical demographic record and more updated data on economic growth, employment, shock events, and other factors. Here, we report the results of two recent experiments of real-time election forecasting: the 2020 general election in Taiwan and six states in the 2020 general election in the United States. Our mostly objective method using ABM may transform how elections are forecasted and studied.
The poll aggregation is conducted in the USA and European democracies for electoral forecasting. However, this has not been the case in Japan because the news media report on electoral campaigns with qualitative assessments rather than poll numbers, although these assessments are based on extensive polling. Our study developed an approach to aggregate qualitative district-level election campaign coverage in Japan, applied the method to forecast the outcomes of the 2017 general election for Japan’s Lower House of the National Diet, and assessed the accuracy of the forecast against the actual results. We integrated the qualitative assessments by using the item response theory, which effectively predicted the electoral results. The method proposed in this paper can be applied to aggregate qualitative assessments by experts in other countries, such as the Cook Political Report in the USA. This would improve the accuracy of election forecasting when combined with existing approaches.
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Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
There are many issues related to distributed data resources, such as data transfer restrictions, privacy, and computation and communication costs. It brings challenges for developing a distributed machine learning approach that can overcome these issues without using complicated machine learning methods, sharing, or centralising the distributed data resources. To address these issues, we proposed a decentralised machine learning approach that allows distributed sites to develop classification models at global and local levels without sharing or moving distributed data to a centralised database or using a central server for iterative computation or communication. Such a decentralised approach restricts the exchanged information between distributed sites with only the trained local models and minimal information. The experimental evaluation conducted on different datasets reported that our approach performed better or close to the centralised or other existing distributed machine learning approaches. Also, we applied a model selection strategy based on F-measure and different combination methods. That is, the proposed method can be used to distributed data resources efficiently and preserve data privacy without sacrificing the predictive performance.
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This article offers an assessment of the current state of US presidential election forecasting models. It pays attention to presidential forecasting models from the last three election cycles. It starts by exploring 'under the hood' and describes the specifics of the most widely known models from the 2004 election. In addition, the predictions made by these models are addressed and the determinants of forecasting accuracy from 1996 to 2004 are identified. Moreover, the article explores the lessons learned from the 2000 campaign and the alternatives to the dominant aggregate-national forecasting models: electronic markets, citizen forecasts, and state-level forecasting models. From a forecasting perspective, the 2008 election outcome was business as usual. Some models were more accurate than others, as is always the case, but the average error was somewhat lower than in the past two elections cycles.
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