Evolving accuracy: A genetic algorithm to improve election night forecasts

Hochreiter, Ronald; Waldhauser, Christoph

doi:10.1016/j.asoc.2015.05.033

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Election night forecasting models generally become a source of insightful information during election coverage in the media while the election outcome is still unknown. Countries including the UK [8,9,37,38], USA [2,10], Spain [37], South Africa [19,20,27], New Zealand [36,37], Australia [37], Austria [25], Sweden [37] and Ireland [37] have, across a number of elections, made use of election night forecasting models to infer an election outcome during hours (and sometimes days) of uncertainty around the final outcome of an election. These models seek to provide an accurate forecast of an election outcome from a small but reasonable sample of the released results.…”

Section: Election Night Forecastsmentioning

confidence: 99%

From SA to the USA: Election forecasting

Holloway¹,

Ittmann

Dudeni-Tlhone³

et al. 2019

ORiON

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Elections draw enormous interest worldwide, especially if these involve major countries, and there is much speculation in the media as to possible outcomes from these elections. In many of these recent elections, such as the UK and USA, however, forecasts from market surveys, electoral polls, scientific forecasting models and even exit polls, obtained from voters as they leave the voting stations, failed to predict the correct outcome. Election night forecasts, which endeavour to forecast the ultimate result before the final outcome is known using early results, were also carried out, with some more accurate than others.After successfully predicting most of the metropolitan region results correctly in the South African local 2016 municipal elections, using an election night forecasting model developed for South Africa (SA), the question of adapting the model to work outside of SA on a different electoral system was raised. The focus of this paper is to describe the results obtained for the 2016 USA presidential election, on election night, using an adapted version of the SA model. This paper also addresses the applicability of the model assumptions as well as the data issues involved in forecasting outside of South Africa. It is shown that even with many hurdles experienced in the process the model performed relatively well.

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Section: Election Night Forecastsmentioning

confidence: 99%

From SA to the USA: Election forecasting

Holloway¹,

Ittmann

Dudeni-Tlhone³

et al. 2019

ORiON

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“…Especially the AIC value is often used when manually selecting the parameters of an AR(I)MA model [19,65]. However, the most popular criterion for optimization in forecasting is accuracy, which can take many forms, such as the Mean Squared Error (MSE) [1,4,5,30,34,54,62], the Mean Absolute Percentage Error (MAPE) [14,23,38,39,42,47,57,63] or the Root Mean Squared Error (RMSE) [22,25,27,38]. In this paper, however, we turn to a profit measure for sales forecasting to optimize the order identification of Seasonal ARIMA models.…”

Section: Introductionmentioning

confidence: 99%

ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting

Calster

Baesens

Lemahieu

2017

Applied Soft Computing

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In forecasting, evolutionary algorithms are often linked to existing forecasting methods to optimize their input parameters. Traditionally, the fitness function of these search heuristics is based on an accuracy measure. In this paper, however, we combine forecasting accuracy with business expertise by defining a flexible and easily interpretable profit function for sales forecasting, which is based on the profit margin of a given product, the volume of its sales and the accuracy of the forecast. ProfARIMA is a new procedure that selects the lags of a Seasonal ARIMA model according to the profit of a model's forecasts by taking advantage of search heuristics. This procedure is tested on both publicly available datasets and a real-life application with datasets of The Coca-Cola Company in order to assess its performance, both in profit and accuracy. Three different evolutionary algorithms were implemented during this testing process, i.e. Genetic Algorithms, Particle Swarm Optimization and Simulated Annealing. The results indicate that ProfARIMA always performs at least equally to the Box-Jenkins methodology and often outperforms this traditional procedure. For The Coca-Cola Company, our new algorithm in combination with Genetic Algorithms even leads to a significantly larger profit for out-of-sample forecasts.

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