Forecasting indoor temperatures during heatwaves: Do more complex models provide better predictions? by Matej Gustin, Rob McLeod, Kevin Lomas

“…Input variables were selected using backward stepwise regressions based on minimisation of the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE), for the ARX and GAM models respectively. …”
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Prediction of internal temperatures during hot summer conditions with time series forecasting models by Matej Gustin, Rob McLeod, Kevin Lomas

“…The study shows that with proper selection of the predictors, based on the Akaike Information Criterion (AIC), the forecasts provide acceptable accuracy for periods up to 72 hours. …”
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Trajectory clustering aided personalized driver intention prediction for intelligent vehicles by Dewei Yi, Jinya Su, Cunjia Liu, Wen-Hua Chen

“…Polynomial regression mixture (PRM) clustering and Akaike’s Information Criterion are applied to individual drivers trajectories for learning in-depth driving behaviours. …”
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Can semi-parametric additive models outperform linear models, when forecasting indoor temperatures in free-running buildings? by Matej Gustin, Rob McLeod, Kevin Lomas

“…Input variables were selected using backward stepwise regressions based on minimisation of the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE), for the ARX and GAM models respectively. …”
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Forecasting indoor temperatures during heatwaves using time series models by Matej Gustin, Rob McLeod, Kevin Lomas

“…The predictor variables were selected by minimising the Akaike Information Criterion (AIC), in order to automatically identify a near-optimal model. …”
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Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods by Ciaran Broderick, Tom Matthews, Robert Wilby, Satish Bastola, Conor Murphy

“…Bayesian Model Averaging (BMA) and the Granger-Ramanathan Averaging (GRA) method were found to outperform the simple arithmetic mean (SAM) and Akaike Information Criteria Averaging (AICA). Here GRA performed better than the best individual model in 51%–86% of cases (according to the Nash-Sutcliffe criterion). …”
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Genetic scores of ENOS, ACE and VEGFA genes are predictive of endothelial dysfunction associated osteoporosis in postmenopausal women by Puneetpal Singh, Monica Singh, Rubanpal Khinda, Srishti Valecha, Nitin Kumar, Surinderpal Singh, Pawan K Juneja, Taranpal Kaur, Sarabjit Mastana

“…Bearers of CTAAAT (OR 2.43, p = 0.007), ACDG (OR 2.50, p = 0.002) and GATA (OR 2.10, p = 0.009) had substantial impact for osteoporosis after correcting the effects with traditional risk factors (TRD).With uncertainty measure (R2h) and Akaike information criterion (AIC), best fit models showed that CTAAAT manifested in multiplicative mode (β ± SE: 2.19 ± 0.86, p < 0.001), whereas ACDG (β ± SE: 1.73 ± 0.54, p = 0.001) and GATA (β ± SE: 3.07 ± 0.81, p < 0.001) expressed in dominant modes. …”
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Comparing efficacy of different climate indices for predicting labor loss, body temperature, and thermal perception in a wide variety of warm and hot climates by George Havenith, James Smallcombe, Simon Hodder, Ollie Jay, Josh Foster

“…Integrating data from earlier studies, data from 982 exposures (75 conditions) exercising at a fixed cardiovascular load of 130b.min-1, in varying temperatures (15-50°C), humidities (20-80%), solar radiation (0-800W.m-2), wind (0.2-3.5m.s-1) and two clothing levels, were used to model the predictive power of ambient temperature, Universal Thermal Climate Index (UTCI), Wet Bulb Globe Temperature (WBGT), Modified Equivalent Temperature (mPET), Heat Index, Apparent Temperature (AT), and Wet Bulb Temperature ( Twb) for the calculation of PWC-loss, skin temperature ( Tskin) and core-to-skin temperature gradient, and Thermal perception( TSV) in the heat. R2, RMSD and Akaike stats were used indicating model performance. …”
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DATASETS HEATSHIELD project Loughborough University, averaged per condition and individual sessions, and files showing the statistical analyses. by George Havenith, Josh Foster, James Smallcombe, Simon Hodder

“…Integrating data from earlier studies, data from 982 exposures (75 conditions) exercising at a fixed cardiovascular load of 130b.min-1, in varying temperatures (15-50°C), humidities (20-80%), solar radiation (0-800W.m-2), wind (0.2-3.5m.s-1) and two clothing levels, were used to model the predictive power of ambient temperature, Universal Thermal Climate Index (UTCI), Wet Bulb Globe Temperature (WBGT), Modified Equivalent Temperature (mPET), Heat Index, Apparent Temperature (AT), and Wet Bulb Temperature (Twb) for the calculation of PWC-loss, skin temperature (Tskin) and core-to-skin temperature gradient, and Thermal perception( TSV) in the heat. R2, RMSD and Akaike stats were used indicating model performance.Indices not including wind/radiation in their calculation (Ta, Heat Index, AT, Twb) struggled to provide consistent predictions across variables. …”
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