A support system for predicting eBay end prices

Heijst, Dennis van; Potharst, Rob; Wezel, Michiel van

doi:10.1016/j.dss.2007.11.004

Cited by 49 publications

(39 citation statements)

References 20 publications

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“…The LSBoost algorithm was chosen because it has a solid mathematical foundation compared with other boosting algorithms. LSBoost begins with an initial guess f_0 and then fits a sequence of M weighted models of T_1 to T_M (decision tree as the base learners in this study) [37]. The final model has the following form:…”

Section: Methodsmentioning

confidence: 99%

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Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance

et al. 2017

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Nitrogen (N) is important for the growth of crops. Estimating leaf nitrogen concentration (LNC) accurately and nondestructively is important for precision agriculture, reduces environmental pollution, and helps model global carbon and N cycles. Leaf reflectance, especially in the visible and near-infrared regions, has been identified as a useful indicator of LNC. Except reflectance passively acquired by spectrometers, the newly developed multispectral LiDAR and hyperspectral LiDAR provide possibilities for measuring leaf spectra actively. The regression relationship between leaf reflectance spectra and rice (Oryza sativa) LNC relies greatly on the algorithm adopted. It would be preferable to find one algorithm that performs well with respect to passive and active leaf spectra. Thus, this study assesses the influence of six popular linear and nonlinear methods on rice LNC retrieval, namely, partial least-square regression, least squares boosting, bagging, random forest, back-propagation neural network (BPNN), and support vector regression of different types/kernels/parameter values. The R 2 , root mean square error and relative error in rice LNC estimation using these different methods were compared through the passive and active spectral measurements of rice leaves of different varieties at different locations and time (Yongyou 4949, Suizhou, 2014, Yangliangyou 6, Wuhan, 2015. Results demonstrate that BPNN provided generally satisfactory performance in estimating rice LNC using the three kinds of passive and active reflectance spectra.

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Section: Methodsmentioning

confidence: 99%

“…Decision trees were used as the individual models that form the ensemble, as is often adopted [37]. Decision trees select important input dimensions in its calibration process.…”

Section: Methodsmentioning

confidence: 99%

Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance

et al. 2017

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“…LSBoost is basically a sequence of simple regression trees, which are called weak learners (B). The objective of LSBoost is to minimize the mean squared error (MSE) between Y and the aggregated prediction of the weak learners (Y pred are combined in a weighted manner [41] to improve model accuracy. The individual regression trees are a function of selected predictor variables (X):…”

Section: Wakeby Parameter Estimation and Modelingmentioning

confidence: 99%

“…In the beginning, the median of the target variables ( Ỹ ) is calculated. Afterwards, multiple regression trees B1, …, Bm are combined in a weighted manner [41] to improve model accuracy. The individual regression trees are a function of selected predictor variables (X):…”

Section: Annual Wind Energy Yield Estimationmentioning

confidence: 99%

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High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

Jung

2016

Energies

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Abstract:In this paper a methodology is presented that can be used to model the annual wind energy yield (AEY mod ) on a high spatial resolution (50 mˆ50 m) grid based on long-term (1979-2010) near-surface wind speed (U S ) time series measured at 58 stations of the German Weather Service (DWD). The study area for which AEY mod is quantified is the German federal state of Baden-Wuerttemberg. Comparability of the wind speed time series was ensured by gap filling, homogenization and detrending. The U S values were extrapolated to the height 100 m (U 100m,emp ) above ground level (AGL) by the Hellman power law. All U 100m,emp time series were then converted to empirical cumulative distribution functions (CDF emp ). 67 theoretical cumulative distribution functions (CDF) were fitted to all CDF emp and their goodness of fit (GoF) was evaluated. It turned out that the five-parameter Wakeby distribution (WK5) is universally applicable in the study area. Prior to the least squares boosting (LSBoost)-based modeling of WK5 parameters, 92 predictor variables were obtained from: (i) a digital terrain model (DTM), (ii) the European Centre for Medium-Range Weather Forecasts re-analysis (ERA)-Interim reanalysis wind speed data available at the 850 hPa pressure level (U 850hPa ), and (iii) the Coordination of Information on the Environment (CORINE) Land Cover (CLC) data. On the basis of predictor importance (PI) and the evaluation of model accuracy, the combination of predictor variables that provides the best discrimination between U 100m,emp and the modeled wind speed at 100 m AGL (U 100m,mod ), was identified. Results from relative PI-evaluation demonstrate that the most important predictor variables are relative elevation (Φ) and topographic exposure (τ) in the main wind direction. Since all WK5 parameters are available, any manufacturer power curve can easily be applied to quantify AEY mod .

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Highly resolved modeling of extreme wind speed in North America and Europe

Jung

Demant

Meyer

et al. 2022

Atmospheric Science Letters

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High wind speed (U) is one of the most dangerous natural hazards in North America and Europe. As a result, spatially explicit, statistical estimation of extreme U is of particular relevance for many sectors. However, the most common sources of wind speed data such as reanalysis data and in situ measurements are limited for this purpose due to their coarse spatial resolution and low representativeness. Thus, the main goal was to develop a high spatial resolution (250 m × 250 m) model (GloWiSMo‐X) for monthly mapping of the maximum hourly U for a 10‐year return period (U10yr) in North America and Europe. The multistep development of GloWiSMo‐X is based on 2544 hourly U time series available from the integrated surface global hourly meteorological data set (UNCEI), U time series from ERA5 (UERA5), and mean wind speed from the Global Wind Speed Model (Utrue¯GloWiSMo). Firstly, the block maxima method was applied to estimate monthly wind speed for a 10‐year return period for both UNCEI (U10yr,NCEI) and UERA5 (U10yr,ERA5). Secondly, the least squares boosting approach was used to predict the target variable U10yr,NCEI yielding the predictions Utruê10yr. The predictor variables U10yr,ERA5, Utrue¯GloWiSMo, continent, and month were used as input. It was found that the highest monthly continental means of Utruê10yr (Utrue¯10yr) in January are 16.4 m/s in North America and 16.3 m/s in Europe. Utrue¯10yr dropped to 13.4 m/s and 12.5 m/s in August. The annual cycle of Utrue¯10yr is more pronounced in Europe than in North America. The central parts of the USA and Western Europe were identified as intracontinental regions with the highest Utrue¯10yr. GloWiSMo‐X proves to be very broadly applicable as it covers two different continents and all months. The model validation by the mean squared error (MSE) demonstrates its improved predictive power compared to ERA5.

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A support system for predicting eBay end prices

Cited by 49 publications

References 20 publications

Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance

Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance

High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

Highly resolved modeling of extreme wind speed in North America and Europe

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