Mean Absolute Percentage Error for regression models

Myttenaere, Arnaud De; Golden, Boris; Grand, Bénédicte Le; Rossi, Fabrice

doi:10.1016/j.neucom.2015.12.114

Cited by 815 publications

(320 citation statements)

References 8 publications

Supporting

Mentioning

312

Contrasting

Unclassified

Order By: Relevance

“…With this approach we have successfully obtained a line comparable to the Least squares line (11) as shown in Figure 4. The Geometric regression line (12) has a lesser GMSE(2.123238) compared to the GMSE(2.7895) of the Least square line(See Table 1).…”

Section: Resultsmentioning

confidence: 82%

See 1 more Smart Citation

Estimation of Regression Coefficients Using Geometric Mean of Squared Error for Single Index Linear Regression Model

Mayilvahanan¹

2016

IJAIA

View full text Add to dashboard Cite

Regression models and their statistical analyses is one of the most important tool the least squares based models in practice may fail to provide optimal results in nonGaussian situations especially when the errors follow distributions with the fat tails. In this paper an unorthodox method of estimating linear regression coefficients by minimising GMSE(geometric mean of squared errors) is explored. Though GMSE(geometric mean of squared errors) is used to compare models it is rarely used to obtain the coefficients. Such a method is tedious to handle due to the large number of roots obtained by minimisation of the loss function. This paper offers a way to tackle that

show abstract

Section: Resultsmentioning

confidence: 82%

“…Squared relative error is also widely used. A detailed explanation of the Least Absolute relative error(LARE) coefficient estimation procedure can be found in [6], [10] & [11].…”

Section: The Least Absolute Relative Error Estimation Methodsmentioning

confidence: 99%

Estimation of Regression Coefficients Using Geometric Mean of Squared Error for Single Index Linear Regression Model

Mayilvahanan¹

2016

IJAIA

View full text Add to dashboard Cite

show abstract

“…After the required input data was obtained, the training session was carried out with the aim of achieving the NN model. Subsequently, the test data set was utilized to determine the predicted probability Markov Chain matrix, such that these results could be compared with the actual results using several measurement errors (Chai and Draxler, 2014;de Myttenaere et al, 2015).…”

Section: Methodsologymentioning

confidence: 99%

Development of Eye Fixation Points Prediction Model from Eye Tracking Data using Neural Network

Moch¹,

Komarudin

Susilo

2017

IJTech

View full text Add to dashboard Cite

Fixation points, as the stopping location of eye movements, can be extracted to generate valuable information about a picture or an object. This information is valuable as it enables the identification of the area/part of the picture that attracts people's attention, which can be used as a consideration when making decisions in the future, for example in marketing. For this reason, in this study, a Neural Network (NN) model was developed to predict the fixation points of a picture. Specifically, the authors experimented with various transfer and training functions in the NN in order to determine which causes the fewest errors. The results show that the method used is applicable in practice since it produces MAPE (Mean Absolute Percent Error) of around 13-15% and MSE (Mean Squared Error) of 0.9-1.1%.

show abstract

“…These indexes are regularly employed in model evaluation studies and their expressions are as follows [40][41][42]:…”

Section: Index Assessmentmentioning

confidence: 99%

Dynamic Monitoring of the Largest Freshwater Lake in China Using a New Water Index Derived from High Spatiotemporal Resolution Sentinel-1A Data

Tian

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Poyang Lake is the largest freshwater lake in China and is well known for its ecological function and economic importance. However, due to the influence of clouds, it is difficult to dynamically monitor the changes in water surface areas using optical remote sensing. To address this problem, we propose a novel method to monitor these changes using Sentinel-1A data. First, the Sentinel-1A water index (SWI) was built using a linear model and a stepwise multiple regression analysis method with Sentinel-1A and Landsat-8 imagery acquired on the same day. Second, water surface areas of Poyang Lake from 24 May 2015 to 14 November 2016 were extracted by the threshold method utilizing time-series SWI data with an interval of 12 days. The results showed that the SWI threshold classification method could be applied to different regions during different periods with high quantity accuracy (approximately 99%). The water surface areas ranged between 1726.73 km 2 and 3729.19 km 2 during the study periods, indicating an extreme variability in the short term. The maximum and average values of the changed areas were 875.57 km 2 (with a change rate of 35%) and 197.58 km 2 (with a change rate of 8.2%), respectively, after 12 days. The changes in the mid-western region of Poyang Lake were more dramatic. These results provide baseline data for high-frequency monitoring of the ecological environment and wetland management in Poyang Lake.

show abstract

Mean Absolute Percentage Error for regression models

Cited by 815 publications

References 8 publications

Estimation of Regression Coefficients Using Geometric Mean of Squared Error for Single Index Linear Regression Model

Estimation of Regression Coefficients Using Geometric Mean of Squared Error for Single Index Linear Regression Model

Development of Eye Fixation Points Prediction Model from Eye Tracking Data using Neural Network

Dynamic Monitoring of the Largest Freshwater Lake in China Using a New Water Index Derived from High Spatiotemporal Resolution Sentinel-1A Data

Contact Info

Product

Resources

About