A pragmatic view of accuracy measurement in forecasting

Flores, Benito E.

doi:10.1016/0305-0483(86)90013-7

Cited by 164 publications

(90 citation statements)

References 5 publications

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“…SMAPE is a dimensionless error measurement which expresses error as a percentage of the actual data and provides an easy way of assessing the errors. It is defined as (Flores 1986):…”

Section: Resultsmentioning

confidence: 99%

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Gholami

Ansari

Hosseini

2016

J Petrol Explor Prod Technol

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Refractive index (RI) provides valuable information about various reservoir engineering calculations, making it a key parameter for characterizing crude oils. Determination of this index through experiment is capitalintensive, time consuming, and also toil. Hence, it is essential to search for an efficient and accurate estimation of crude oil RI. In this study, an intelligent approach, based on optimized support vector regression (SVR), is introduced to find a quantitative correlation between crude oil RI and SARA (saturate, aromatic, resin, and asphaltene) fraction data. Optimization of SVR is implemented through three searching approaches, viz. hybrid of grid and pattern search (HGP), genetic algorithm (GA), and imperialist competitive algorithm (ICA). Using these approaches, three models are constructed and tested on experimental data gathered from open source literature. To evaluate the performance of these models, their outputs are compared with corresponding experimental data in terms of statistical criteria. The comparative study clearly shows the advantage of ICA over its rivals (GA and HGP) in optimizing the SVR parameters. ICA optimized support vector regression results in an R 2 of 0.9971 and MSE of 1.48548e-05 demonstrating its efficacy in obtaining crude oil refractive index form SARA data.

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“…SMAPE is a dimensionless error measurement which expresses error as a percentage of the actual data and provides an easy way of assessing the errors. It is defined as (Flores 1986):…”

Section: Resultsmentioning

confidence: 99%

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Gholami

Ansari

Hosseini

2016

J Petrol Explor Prod Technol

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“…• SMAPE: Symmetric Mean Absolute Percentage Error [11] measures the relative error between the predicted and true value. This is a useful alternative to the more common mean-squared error as it can compare outcome variables that have different ranges.…”

Section: Methodsmentioning

confidence: 99%

Estimating county health statistics with twitter

Culotta

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

132

105

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Understanding the relationships among environment, behavior, and health is a core concern of public health researchers. While a number of recent studies have investigated the use of social media to track infectious diseases such as influenza, little work has been done to determine if other health concerns can be inferred. In this paper, we present a large-scale study of 27 health-related statistics, including obesity, health insurance coverage, access to healthy foods, and teen birth rates. We perform a linguistic analysis of the Twitter activity in the top 100 most populous counties in the U.S., and find a significant correlation with 6 of the 27 health statistics. When compared to traditional models based on demographic variables alone, we find that augmenting models with Twitter-derived information improves predictive accuracy for 20 of 27 statistics, suggesting that this new methodology can complement existing approaches.

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“…This criterion evaluates the prediction error as a percentage of the actual or target DP value using Equation (9) [24,25].…”

Section: Symmetric Mean Absolute Percentage Error (Smape)mentioning

confidence: 99%

Transformer Paper Expected Life Estimation Using ANFIS Based on Oil Characteristics and Dissolved Gases (Case Study: Indonesian Transformers)

et al. 2017

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Abstract:This article presents an algorithm for modelling an Adaptive Neuro Fuzzy Inference System (ANFIS) for power transformer paper conditions in order to estimate the transformer's expected life. The dielectric characteristics, dissolved gasses, and furfural of 108 running transformers were collected, which were divided into 76 training datasets and another 32 testing datasets. The degree of polymerization (DP) of the transformer paper was predicted using the ANFIS model based on using the dielectric characteristics and dissolved gases as input. These inputs were analyzed, and the best combination was selected, whereas CO + CO 2 , acidity, interfacial tension, and color were correlated with the paper's deterioration condition and were chosen as the input variables. The best combination of input variables and membership function was selected to build the optimal ANFIS model, which was then compared and evaluated. The proposed ANFIS model has 89.07% training accuracy and 85.75% testing accuracy and was applied to a transformer paper insulation assessment and an estimation of the expected life of four Indonesian transformers for which furfural data is unavailable. This proposed algorithm can be used as a furfural alternative for the general assessment of transformer paper conditions and the estimation of expected life and provides a helpful assistance for experts in transformer condition assessment.

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A pragmatic view of accuracy measurement in forecasting

Cited by 164 publications

References 5 publications

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Estimating county health statistics with twitter

Transformer Paper Expected Life Estimation Using ANFIS Based on Oil Characteristics and Dissolved Gases (Case Study: Indonesian Transformers)

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