Forecasting elections results via the voter model with stubborn nodes

AbstractIn this paper we propose a novel method to forecast the result of elections using only official results of previous ones. It is based on the voter model with stubborn nodes and uses theoretical results developed in a previous work of ours. We look at popular vote shares for the Conservative and Labour parties in the UK and the Republican and Democrat parties in the US. We are able to perform time-evolving estimates of the model parameters and use these to forecast the vote shares for each party in any election. We obtain a mean absolute error of 4.74%. As a side product, our parameters estimates provide meaningful insight on the political landscape, informing us on the proportion of voters that are strong supporters of each of the considered parties.

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Explanation Plus Prediction—The Logical Focus of Project Management Research

Project Management Journal ◽

10.1177/8756972821999945 ◽

2021 ◽

pp. 875697282199994

Author(s):

Joseph F. Hair ◽

Marko Sarstedt

Keyword(s):

Project Management ◽

Statistical Models ◽

Predictive Power ◽

Mean Absolute Error ◽

Explanatory Power ◽

Absolute Error ◽

Model Parameters ◽

Mean Square ◽

Management Research ◽

The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

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A Maximum Entropy Production Evaporation – Transpiration Product for Australia

10.5194/essd-2019-70 ◽

2019 ◽

Author(s):

Olanrewaju Abiodun ◽

Okke Batelaan ◽

Huade Guan ◽

Jingfeng Wang

Keyword(s):

Entropy Production ◽

Maximum Entropy ◽

Mean Absolute Error ◽

Absolute Error ◽

Production Model ◽

Model Parameters ◽

Drought Period ◽

Maximum Entropy Production ◽

Millennium Drought ◽

Mean Square Errors

Abstract. The aim of this research is to develop evaporation and transpiration products for Australia based on the maximum entropy production model (MEP). We introduce a method into the MEP algorithm of estimating the required model parameters over the entire Australia through the use of pedotransfer function, soil properties and remotely sensed soil moisture data. Our algorithm calculates the evaporation and transpiration over Australia on daily timescales at the 5 km2 resolution for 2003–2013. The MEP evapotranspiration (ET) estimates are validated using observed ET data from 20 Eddy Covariance (EC) flux towers across 8 land cover types in Australia. We also compare the MEP ET at the EC flux towers with two other ET products over Australia; MOD16 and AWRA-L products. The MEP model outperforms the MOD16 and AWRA-L across the 20 EC flux sites, with average root mean square errors (RMSE), 8.21, 9.87 and 9.22 mm/8 days respectively. The average mean absolute error (MAE) for the MEP, MOD16 and AWRA-L are 6.21, 7.29 and 6.52 mm/8 days, the average correlations are 0.64, 0.57 and 0.61, respectively. The percentage Bias of the MEP ET was within 20 % of the observed ET at 12 of the 20 EC flux sites while the MOD16 and AWRA-L ET were within 20 % of the observed ET at 4 and 10 sites respectively. Our analysis shows that evaporation and transpiration contribute 38 % and 62 %, respectively, to the total ET across the study period which includes a significant part of the “millennium drought” period (2003–2009) in Australia. The data (Abiodun et al., 2019) is available at https://doi.org/10.25901/5ce795d313db8.

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0433 Targeting Light Sensitivity Parameters to Optimize Circadian Phase Predictions

SLEEP ◽

10.1093/sleep/zsaa056.430 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A166-A166

Author(s):

J E Stone ◽

E M McGlashan ◽

S W Cain ◽

A J Phillips

Keyword(s):

Sensitivity Analysis ◽

Circadian Clock ◽

Mean Absolute Error ◽

Light Response ◽

Absolute Error ◽

Light Sensitivity ◽

Model Parameters ◽

Circadian Period ◽

Individual Level ◽

Circadian Phase

Abstract Introduction Existing models of the human circadian clock accurately predict phase at group-level but not at individual-level. Interindividual variability in light sensitivity is not currently accounted for in these models and may be a practical approach to improving individual-level predictions. Using the gold-standard predictive model, we (i) identified whether varying light sensitivity parameters produces meaningful changes in predicted phase in field conditions; and (ii) tested whether optimizing parameters can significantly improve accuracy of circadian phase prediction. Methods Healthy participants (n=12, 7 women, aged 18-26) underwent continuous light and activity monitoring for 3 weeks (Actiwatch Spectrum). Salivary dim light melatonin onset (DLMO) was measured each week. A model of the human circadian clock and its response to light was used to predict the three weekly DLMO times using the individual’s light data. A sensitivity analysis was performed varying three model parameters within physiological ranges: (i) amplitude of the light response [p]; (ii) advance vs. delay bias of the light response [K]; and (iii) intrinsic circadian period [tau]. These parameters were then fitted using least squares estimation to obtain optimal predictions of DLMO for each individual. Accuracy was compared between optimized parameters and default parameters. Results The default model predicted DLMO with mean absolute error of 1.02h. Sensitivity analysis showed the average range of variation in predicted DLMOs across participants was 0.65h for p, 4.28h for K and 3.26h for tau. Fitting parameters independently, we found mean absolute error of 0.85h for p, 0.71h for K and 0.75h for tau. Fitting p and K together reduced mean absolute error to 0.57h. Conclusion Light sensitivity parameters capture similar or greater variability in phase as intrinsic circadian period, indicating they are a viable option for individualising circadian phase predictions. Future prospective work is needed using measures of light sensitivity to validate this approach. Support N/A

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A Novel Method for Estimating Throwing Speed in Handball using a Wearable Device

10.20944/preprints202008.0085.v1 ◽

2020 ◽

Author(s):

Sebastian D Skejø ◽

Jesper Bencke ◽

Merete Møller ◽

Henrik Sørensen

Keyword(s):

Cross Validation ◽

Prediction Models ◽

Mean Absolute Error ◽

Low Cost ◽

Absolute Error ◽

Shoulder Injuries ◽

Specific Load ◽

Cheap Method ◽

Novel Method ◽

3D Motion Capture

Understanding the shoulder-specific load in handball is important for both prevention and rehabilitation of shoulder injuries. The shoulder-specific load is largely a result of the number and speed of throws. However, it is difficult to quantify number and speed of throws in handball due to limitations in the current technology. Therefore, the purpose of this study was to develop a novel method to estimate throwing speed in handball using a low-cost accelerometer-based device. Nineteen experienced handball players each performed 25 throws of varying types while we measured the acceleration of the wrist using the accelerometer and the throwing speed using 3D motion capture. Using cross-validation, we developed four prediction models using combinations of the logarithm of the peak total acceleration, sex and throwing type as the predictor and the throwing speed as the outcome. We found that all models were well-calibrated (mean calibration of all models: 0.0 m/s, calibration slope range: 0.99-1.00) and precise (R2 = 0.71-0.85, mean absolute error = 1.32-1.82 m/s). We conclude that the developed method appear to provide practitioners and researchers with a feasible and cheap method to estimate throwing speeds in handball.

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Automatically steering cardiac catheters in vivo with respiratory motion compensation

The International Journal of Robotics Research ◽

10.1177/0278364920903785 ◽

2020 ◽

Vol 39 (5) ◽

pp. 586-597 ◽

Cited By ~ 1

Author(s):

Paul M Loschak ◽

Alperen Degirmenci ◽

Cory M Tschabrunn ◽

Elad Anter ◽

Robert D Howe

Keyword(s):

Motion Compensation ◽

Respiratory Motion ◽

Mean Absolute Error ◽

Absolute Error ◽

Respiratory Motion Compensation ◽

X Ray ◽

Cardiac Catheters ◽

The Us ◽

Predictive Filtering

A robotic system for automatically navigating ultrasound (US) imaging catheters can provide real-time intra-cardiac imaging for diagnosis and treatment while reducing the need for clinicians to perform manual catheter steering. Clinical deployment of such a system requires accurate navigation despite the presence of disturbances including cyclical physiological motions (e.g., respiration). In this work, we report results from in vivo trials of automatic target tracking using our system, which is the first to navigate cardiac catheters with respiratory motion compensation. The effects of respiratory disturbances on the US catheter are modeled and then applied to four-degree-of-freedom steering kinematics with predictive filtering. This enables the system to accurately steer the US catheter and aim the US imager at a target despite respiratory motion disturbance. In vivo animal respiratory motion compensation results demonstrate automatic US catheter steering to image a target ablation catheter with 1.05 mm and 1.33° mean absolute error. Robotic US catheter steering with motion compensation can improve cardiac catheterization techniques while reducing clinician effort and X-ray exposure.

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Parameterization dependence of the hydrological cycle in a general circulation model of intermediate complexity

10.5194/egusphere-egu21-1328 ◽

2021 ◽

Author(s):

Oliver Mehling ◽

Elisa Ziegler ◽

Heather Andres ◽

Martin Werner ◽

Kira Rehfeld

Keyword(s):

General Circulation Model ◽

General Circulation ◽

Hydrological Cycle ◽

Mean Absolute Error ◽

Circulation Model ◽

Absolute Error ◽

Bayesian Optimization ◽

Model Parameters ◽

Climate Projections ◽

Intermediate Complexity

The global hydrological cycle is of crucial importance for life on Earth. Hence, it is a focus of both future climate projections and paleoclimate modeling. The latter typically requires long integrations or large ensembles of simulations, and therefore models of reduced complexity are needed to reduce the computational cost. Here, we study the hydrological cycle of the the Planet Simulator (PlaSim) [1], a general circulation model (GCM) of intermediate complexity, which includes evaporation, precipitation, soil hydrology, and river advection.Using published parameter configurations for T21 resolution [2, 3], PlaSim strongly underestimates precipitation in the mid-latitudes as well as global atmospheric water compared to ERA5 reanalysis data [4]. However, the tuning of PlaSim has been limited to optimizing atmospheric temperatures and net radiative fluxes so far [3].Here, we present a different approach by tuning the model&#8217;s atmospheric energy balance and water budget simultaneously. We argue for the use of the globally averaged mean absolute error (MAE) for 2 m temperature, net radiation, and evaporation in the objective function. To select relevant model parameters, especially with respect to radiation and the hydrological cycle, we perform a sensitivity analysis and evaluate the feature importance using a Random Forest regressor. An optimal set of parameters is obtained via Bayesian optimization.Using the optimized set of parameters, the mean absolute error of temperature and cloud cover is reduced on most model levels, and mid-latitude precipitation patterns are improved. In addition to annual zonal-mean patterns, we examine the agreement with the seasonal cycle and discuss regions in which the bias remains considerable, such as the monsoon region over the Pacific.We discuss the robustness of this tuning with regards to resolution (T21, T31, and T42), and compare the atmosphere-only results to simulations with a mixed-layer ocean. Finally, we provide an outlook on the applicability of our parametrization to climate states other than present-day conditions.[1] K. Fraedrich et al., Meteorol. Z. 14, 299&#8211;304 (2005) [2] F. Lunkeit et al., Planet Simulator User&#8217;s Guide Version 16.0 (University of Hamburg, 2016) [3] G. Lyu et al., J. Adv. Model. Earth Syst. 10, 207&#8211;222 (2018) [4] H. Hersbach et al., Q. J. R. Meteorol. Soc. 146, 1999&#8211;2049 (2020)

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Estimating Throwing Speed in Handball Using a Wearable Device

Sensors ◽

10.3390/s20174925 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4925

Author(s):

Sebastian D. Skejø ◽

Jesper Bencke ◽

Merete Møller ◽

Henrik Sørensen

Keyword(s):

Cross Validation ◽

Prediction Models ◽

Mean Absolute Error ◽

Low Cost ◽

Absolute Error ◽

Specific Load ◽

Cheap Method ◽

Novel Method ◽

3D Motion Capture ◽

Acceleration Signals

Throwing speed is likely a key determinant of shoulder-specific load. However, it is difficult to estimate the speed of throws in handball in field-based settings with many players due to limitations in current technology. Therefore, the purpose of this study was to develop a novel method to estimate throwing speed in handball using a low-cost accelerometer-based device. Nineteen experienced handball players each performed 25 throws of varying types while we measured the acceleration of the wrist using the accelerometer and the throwing speed using 3D motion capture. Using cross-validation, we developed four prediction models using combinations of the logarithm of the peak total acceleration, sex and throwing type as the predictor and the throwing speed as the outcome. We found that all models were well-calibrated (mean calibration of all models: 0.0 m/s, calibration slope of all models: 1.00) and precise (R2 = 0.71–0.86, mean absolute error = 1.30–1.82 m/s). We conclude that the developed method provides practitioners and researchers with a feasible and cheap method to estimate throwing speed in handball from segments of wrist acceleration signals containing only a single throw.

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Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead

Bioengineering ◽

10.3390/bioengineering8060084 ◽

2021 ◽

Vol 8 (6) ◽

pp. 84

Author(s):

Kathleen Carvalho ◽

João Paulo Vicente ◽

Mihajlo Jakovljevic ◽

João Paulo Ramos Teixeira

Keyword(s):

Mean Absolute Error ◽

Absolute Error ◽

Medical Studies ◽

Virus Propagation ◽

Icu Patients ◽

The United Kingdom ◽

The Mean ◽

The Uk ◽

Vaccination Campaigns

The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were two proposed methodologies in making predictions 28 days ahead for the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, the United Kingdom, and Germany. A case study of the results of massive immunization in Israel was also considered. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries’ size and the cumulative vaccination values by the percentage of population immunized (with at least one dose of the vaccine). As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology, targeting other possibilities of use for the method proposed. The best architecture achieved a general MAE for the 1-to-28-day ahead forecast, which is lower than 30 cases, 0.6 deaths, and 2.5 ICU patients per million people.

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The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study

Sensors ◽

10.3390/s21072361 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2361

Author(s):

Giovanni Delnevo ◽

Giacomo Mancini ◽

Marco Roccetti ◽

Paola Salomoni ◽

Elena Trombini ◽

...

Keyword(s):

Machine Learning ◽

Body Mass Index ◽

Body Mass ◽

Mean Absolute Error ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Psychological Variables ◽

Novel Method ◽

The Relationship

This study investigates on the relationship between affect-related psychological variables and Body Mass Index (BMI). We have utilized a novel method based on machine learning (ML) algorithms that forecast unobserved BMI values based on psychological variables, like depression, as predictors. We have employed various machine learning algorithms, including gradient boosting and random forest, with psychological variables relative to 221 subjects to predict both the BMI values and the BMI status (normal, overweight, and obese) of those subjects. We have found that the psychological variables in use allow one to predict both the BMI values (with a mean absolute error of 5.27–5.50) and the BMI status with an accuracy of over 80% (metric: F1-score). Further, our study has also confirmed the particular efficacy of psychological variables of negative type, such as depression for example, compared to positive ones, to achieve excellent predictive BMI values.

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Grid search of multilayer perceptron based on the walk-forward validation methodology

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i2.pp1742-1751 ◽

2021 ◽

Vol 11 (2) ◽

pp. 1742

Author(s):

Tran Thanh Ngoc ◽

Le Van Dai ◽

Dang Thi Phuc

Keyword(s):

Neural Network ◽

Multilayer Perceptron ◽

Mean Absolute Error ◽

Absolute Error ◽

Ho Chi Minh City ◽

Percentage Error ◽

Grid Search ◽

Load Demand ◽

The Us ◽

Airline Passenger

Multilayer perceptron neural network is one of the widely used method for load forecasting. There are hyperparameters which can be used to determine the network structure and used to train the multilayer perceptron neural network model. This paper aims to propose a framework for grid search model based on the walk-forward validation methodology. The training process will specify the optimal models which satisfy requirement for minimum of accuracy scores of root mean square error, mean absolute percentage error and mean absolute error. The testing process will evaluate the optimal models along with the other ones. The results indicated that the optimal models have accuracy scores near the minimum values. The US airline passenger and Ho Chi Minh city load demand data were used to verify the accuracy and reliability of the grid search framework.

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