Relevant bibliographies by topics / Mean absolute error (MAE)

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers
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Academic literature on the topic 'Mean absolute error (MAE)'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Mean absolute error (MAE)"

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Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, no. 1 (February 28, 2014): 1525–34. http://dx.doi.org/10.5194/gmdd-7-1525-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to the problem. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric.
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Hodson, Timothy O. "Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not." Geoscientific Model Development 15, no. 14 (July 19, 2022): 5481–87. http://dx.doi.org/10.5194/gmd-15-5481-2022.

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Abstract. The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.
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Robeson, Scott M., and Cort J. Willmott. "Decomposition of the mean absolute error (MAE) into systematic and unsystematic components." PLOS ONE 18, no. 2 (February 17, 2023): e0279774. http://dx.doi.org/10.1371/journal.pone.0279774.

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When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean absolute error (MAE) are more interpretable than MSE or RMSE. Part of that historical preference for sums-of-squares measures is that they are mathematically amenable to decomposition and one can then form ratios, such as those based on separating MSE into its systematic and unsystematic components. Here, we develop and illustrate a decomposition of MAE into three useful submeasures: (1) bias error, (2) proportionality error, and (3) unsystematic error. This three-part decomposition of MAE is preferable to comparable decompositions of MSE because it provides more straightforward information on the nature of the model-error distribution. We illustrate the properties of our new three-part decomposition using a long-term reconstruction of streamflow for the Upper Colorado River.
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Suryanto, Andik Adi. "PENERAPAN METODE MEAN ABSOLUTE ERROR (MEA) DALAM ALGORITMA REGRESI LINEAR UNTUK PREDIKSI PRODUKSI PADI." SAINTEKBU 11, no. 1 (February 8, 2019): 78–83. http://dx.doi.org/10.32764/saintekbu.v11i1.298.

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Mean Absolute Error (MAE) adalah dua diantara banyak metode untuk mengukur tingkat keakuratan suatu model peramalan. Nilai MAE merepresentasikan rata – rata kesalahan (error) absolut antara hasil peramalan dengan nilai sebenarnya. Dengan menggunakan algoritma regresi linear dapat memberikan nilai prediksi produksi padi dengan 2 variabel jumlah pertumbuhan penduduk dan jumlah produksi padi pertahun, sedangan keakuratan dari hasil perhitungan prediksi menggunakan metode Mean Absolute Error (MAE) yang gunakan untuk mengukur tingkat keakuratan suatu model peramalan. Padi adalah salah satu kebutuhan pokok untuk memenuhi kebutuhan karbohidrat bagi penduduk. Dengan meningkatnya pertumbuhan penduduk tiap tahunnya dan kegiatan sosial ekonomi yang menyertainya kebutuhan Produksi padi makin meningkat pula berbanding lurus jumlah penduduk dan kegiatan ekonomi. Kata Kunci: Produksi, Padi, Regresi Linear, Mean Absolute Error
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Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature." Geoscientific Model Development 7, no. 3 (June 30, 2014): 1247–50. http://dx.doi.org/10.5194/gmd-7-1247-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.
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Wang, Weijie, and Yanmin Lu. "Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model." IOP Conference Series: Materials Science and Engineering 324 (March 2018): 012049. http://dx.doi.org/10.1088/1757-899x/324/1/012049.

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Willmott, CJ, and K. Matsuura. "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance." Climate Research 30 (2005): 79–82. http://dx.doi.org/10.3354/cr030079.

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HOSSEN, Sayed Mohibul, Mohd Tahir ISMAIL, Mosab I. TABASH, and Ahmed ABOUSAMAK. "ACCRUED FORECASTING ON TOURIST’S ARRIVAL IN BANGLADESH FOR SUSTAINABLE DEVELOPMENT." GeoJournal of Tourism and Geosites 36, no. 2spl (June 30, 2021): 708–14. http://dx.doi.org/10.30892/gtg.362spl19-701.

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Forecasting of potential tourists’ appearance could assume a critical role in the tourism industry, arranging at all levels in both the private and public sectors. In this study our aim to build an econometric model to forecast worldwide visitor streams to Bangladesh. For this purpose, the present investigation focuses on univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling. Model choice criteria were Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (RMSE). As per descriptive statistics, the mean appearances were 207012 and will be 656522 (application) every year. Mean Absolute Deviation and Mean Squared Deviation likewise concurred with MAPE, MAE, and MSE. The result reveals that for sustainable development the SARIMA model is the reasonable model for forecasting universal visitor appearances in Bangladesh.
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Richasdy, Donni, and Saiful Akbar. "Path Smoothing With Support Vector Regression." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (July 20, 2020): 142–50. http://dx.doi.org/10.31289/jite.v4i1.3856.

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One of moving object problems is the incomplete data that acquired by Geo-tracking technology. This phenomenon can be found in aircraft ground-based tracking with data loss come near to 5 minutes. It needs path smoothing process to complete the data. One solution of path smoothing is using physics of motion, while this research performs path smoothing process using machine learning algorithm that is Support Vector Regression (SVR). This study will optimize the SVR configuration parameters such as kernel, common, gamma, epsilon and degree. Support Vector Regression will predict value of the data lost from aircraft tracking data. We use combination of mean absolute error (MAE) and mean absolute percentage error (MAPE) to get more accuracy. MAE will explain the average value of error that occurs, while MAPE will explain the error percentage to the data. In the experiment, the best error value MAE 0.52 and MAPE 2.07, which means error data ± 0.52, this is equal to 2.07% of the overall data value.Keywords: Moving Object, Path Smoothing, Support Vector Regression, MAE
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Martinello, Larissa Maria, Samuel Bellido Rodrigues, Tásia Hickmann, Jairo Marlon Corrêa, and Levi Lopes Teixeira. "Estudo comparativo entre os modelos de previsão ARIMA e ETS para dados temporais da produção de leite no Brasil." Revista do Instituto de Laticínios Cândido Tostes 76, no. 1 (December 31, 2021): 12–27. http://dx.doi.org/10.14295/2238-6416.v76i1.823.

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A produção de leite está em constante crescimento, pois movimenta a economia e é fonte de renda para diversas famílias. Um eficaz planejamento das atividades executadas tanto por parte dos produtores de leite como dos laticínios está diretamente relacionado às expectativas em relação à produção anual do leite. A estimativa da produção de leite pode ser abordada por meio de modelos numérico-estatísticos de previsão, com auxílio de softwares como o R. Dessa forma, o presente artigo aborda uma análise comparativa da previsão de produção de leite industrializado no Brasil, por meio dos modelos ARIMA (Autoregressive Integrated Moving Average – Autorregressivo Integrado de Médias Móveis) e ETS (Error, Trend, Seasonal – Erro, Tendência, Sazonal). A determinação dos modelos e demais cálculos estatísticos foram realizados por meio do software livre R para séries de dados mensais e trimestrais da produção de leite, obtido pelo site do IBGE, no período de 2004 a 2018. Os modelos forneceram as previsões para o ano de 2019 e estes foram comparados com valores reais. As métricas utilizadas foram o MAPE (Mean Absolute Percentage Error – Erro Percentual Médio Absoluto), RMSE (Root Mean Square Error – Raiz do Erro Médio Quadrático) e MAE (Mean Absolute Error – Erro Médio Absoluto), as quais indicam que o modelo ARIMA apresentou maior acurácia para ambas as séries analisadas.
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Dissertations / Theses on the topic "Mean absolute error (MAE)"

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Loce, Robert P. "Morphological filter mean-absolute-error representation theorems and their application to optimal morphological filter design /." Online version of thesis, 1993. http://hdl.handle.net/1850/11065.

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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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(9805406), Md Rahat Hossain. "A novel hybrid method for solar power prediction." Thesis, 2013. https://figshare.com/articles/thesis/A_novel_hybrid_method_for_solar_power_prediction/13432601.

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Renewable energy sources, particularly solar energy, play a vital role for generating environment-friendly electricity. Foremost advantages of solar energy sources are: nonpolluting, free in terms of availability and renewable. The renewable green-energy sources are becoming more cost-effective and sustainable substitutes to conventional fossil fuels. Nonetheless, power generation from Photovoltaic (PV) systems is unpredictable due to its reliance on meteorological conditions. The effective use of this fluctuating solar energy source obliges reliable and robust forecast information for management and operation of a contemporary power grid. Due to the remarkable proliferation of solar power generation, the prediction of solar power yields becomes more and more imperative. Large-scale penetration of solar power in the electricity grid provides numerous challenges to the grid operator, mainly due to the intermittency of the sun. Since the power produced by a PV depends decisively on the unpredictability of the sun, unexpected variations of a PV output may increase operating costs for the electricity system as well as set potential threats to the reliability of electricity supply. Nevertheless, the prediction accuracy level of the existing prediction methods for solar power is not up to the mark that is very much required to deal with the forthcoming sophisticated and advanced power grid like Smart Grid. Therefore, accurate solar power prediction methods become very substantial. The main goal of this thesis is to produce a novel hybrid prediction method for more accurate, reliable and robust solar power prediction using modern Computational Intelligence (CI). The hybrid prediction method which is mainly composed of multiple regressive machine learning techniques will be as accurate and reliable as possible, to accommodate the needs of any future systems that depend upon it for generator or load scheduling, or grid stability control applications. In this thesis, research on the experimental analysis and development of hybrid machine learning for solar power prediction has been presented. The thesis makes the following major contributions: 1) It investigates heterogeneous machine learning techniques for hybrid prediction methods for solar power 2) It applies feature selection methods to individually improve the prediction accuracy of previous machine learning techniques 3) It investigates possible parameter optimisation of computational intelligence techniques to make sure that individual predictions are as accurate as possible 4) It proposes hybrid prediction by non-linearly integrating the discrete prediction results from various machine-learning techniques. Performance characteristics of the hybrid machine learning over individuals was carried out through experimental analysis and the results are justified by various statistical tests and error validation metrics which confirmed the maximum achievable accuracy of the developed hybrid method for solar power prediction. It is expected that the outcome of the research will provide noteworthy contribution to the relevant research field as well as to Australian power industries in the near future.
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Book chapters on the topic "Mean absolute error (MAE)"

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Mean Absolute Error." In Encyclopedia of Machine Learning, 652. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_525.

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Tomaselli, Venera, and Giulio Giacomo Cantone. "Multipoint vs slider: a protocol for experiments." In Proceedings e report, 91–96. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.19.

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Since the broad diffusion of Computer-Assisted survey tools (i.e. web surveys), a lively debate about innovative scales of measure arose among social scientists and practitioners. Implications are relevant for applied Statistics and evaluation research since while traditional scales collect ordinal observations, data from sliders can be interpreted as continuous. Literature, however, report excessive times of completion of the task from sliders in web surveys. This experimental protocol is aimed at testing hypotheses on the accuracy in prediction and dispersion of estimates from anonymous participants who are recruited online and randomly assigned into tasks in recognition of shades of colour. The treatment variable is two scales: a traditional multipoint 0-10 multipoint vs a slider 0-100. Shades have a unique parametrisation (true value) and participants have to guess the true value through the scale. These tasks are designed to recreate situations of uncertainty among participants while minimizing the subjective component of a perceptual assessment and maximizing information about scale-driven differences and biases. We propose to test statistical differences in the treatment variable: (i) mean absolute error from the true value (ii), time of completion of the task. To correct biases due to the variance in the number of completed tasks among participants, data about participants can be collected through both pre-tasks acceptance of web cookies and post-tasks explicit questions.
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Li, Fangjun, and Gao Niu. "US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model." In Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning, 177–207. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8455-2.ch007.

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For the purpose of control health expenditures, there are some papers investigating the characteristics of patients who may incur high expenditures. However fewer papers are found which are based on the overall medical conditions, so this chapter was to find a relationship among the prevalence of medical conditions, utilization of healthcare services, and average expenses per person. The authors used bootstrapping simulation for data preprocessing and then used linear regression and random forest methods to train several models. The metrics root mean square error (RMSE), mean absolute percent error (MAPE), mean absolute error (MAE) all showed that the selected linear regression model performs slightly better than the selected random forest regression model, and the linear model used medical conditions, type of services, and their interaction terms as predictors.
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Rajkumar S., Mary Nikitha K., Ramanathan L., Rajasekar Ramalingam, and Mudit Jantwal. "Cloud Hosted Ensemble Learning-Based Rental Apartment Price Prediction Model Using Stacking Technique." In Deep Learning Research Applications for Natural Language Processing, 229–38. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6001-6.ch015.

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In this chapter, online rental listings of the city of Hyderabad are used as a data source for mapping house rent. Data points were scraped from one of the popular Indian rental websites www.nobroker.in. With the collected information, models of rental market dynamics were developed and evaluated using regression and boosting algorithms such as AdaBoost, CatBoost, LightGBM, XGBoost, KRR, ENet, and Lasso regression. An ensemble machine learning algorithm of the best combination of the aforementioned algorithms was also implemented using the stacking technique. The results of these algorithms were compared using several performance metrics such as coefficient of determination (R2 score), mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and accuracy in order to determine the most effective model. According to further examination of results, it is clear that the ensemble machine learning algorithm does outperform the others in terms of better accuracy and reduced errors.
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Rapoo, Mogari Ishmael, Martin M. Chanza, and Gomolemo Motlhwe. "Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network." In Research Anthology on Macroeconomics and the Achievement of Global Stability, 551–67. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7460-0.ch030.

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This study examines the performance of seasonal autoregressive integrated moving average (SARIMA), multilayer perceptron neural networks (MLPNN), and hybrid SARIMA-MLPNN model(s) in modelling and forecasting inflation rate using the monthly consumer price index (CPI) data from 2010 to 2019 obtained from the South African Reserve Bank (SARB). The forecast errors in inflation rate forecasting are analyzed and compared. The study employed root mean squared error (RMSE) and mean absolute error (MAE) as performance measures. The results indicate that significant improvements in forecasting accuracy are obtained with the hybrid model (SARIMA-MLPNN) compared to the SARIMA and MLPNN. The MLPNN model outperformed the SARIMA model. However, the hybrid SARIMA-MLPNN model outperformed both the SARIMA and MLPNN in terms of forecasting accuracy/accuracy performance.
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Rapoo, Mogari Ishmael, Martin M. Chanza, and Gomolemo Motlhwe. "Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network." In Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning, 306–22. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8455-2.ch012.

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This study examines the performance of seasonal autoregressive integrated moving average (SARIMA), multilayer perceptron neural networks (MLPNN), and hybrid SARIMA-MLPNN model(s) in modelling and forecasting inflation rate using the monthly consumer price index (CPI) data from 2010 to 2019 obtained from the South African Reserve Bank (SARB). The forecast errors in inflation rate forecasting are analyzed and compared. The study employed root mean squared error (RMSE) and mean absolute error (MAE) as performance measures. The results indicate that significant improvements in forecasting accuracy are obtained with the hybrid model (SARIMA-MLPNN) compared to the SARIMA and MLPNN. The MLPNN model outperformed the SARIMA model. However, the hybrid SARIMA-MLPNN model outperformed both the SARIMA and MLPNN in terms of forecasting accuracy/accuracy performance.
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Lahmiri, Salim. "An Exploration of Backpropagation Numerical Algorithms in Modeling US Exchange Rates." In Advances in Business Information Systems and Analytics, 380–96. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7272-7.ch022.

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This chapter applies the Backpropagation Neural Network (BPNN) trained with different numerical algorithms and technical analysis indicators as inputs to forecast daily US/Canada, US/Euro, US/Japan, US/Korea, US/Swiss, and US/UK exchange rate future price. The training algorithms are the Fletcher-Reeves, Polak-Ribiére, Powell-Beale, quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), and the Levenberg-Marquardt (LM). The standard Auto Regressive Moving Average (ARMA) process is adopted as a reference model for comparison. The performance of each BPNN and ARMA process is measured by computing the Mean Absolute Error (MAE), Mean Absolute Deviation (MAD), and Mean of Squared Errors (MSE). The simulation results reveal that the LM algorithm is the best performer and show strong evidence of the superiority of the BPNN over ARMA process. In sum, because of the simplicity and effectiveness of the approach, it could be implemented for real business application problems to predict US currency exchange rate future price.
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Nan, Linjiang, Mingxiang Yang, Honggang Li, Zhen Guan, Hejia Wang, Weihua Xiao, and Ningpeng Dong. "Applicability Assessment of Multi-Source Satellite Precipitation Products in Lancang River Basin." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220958.

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In order to evaluate the applicability of Fengyun 2G and GPM IMERG satellite precipitation products in the Lancang River Basin, four evaluation indicators are selected in this paper. The results show that: (1) In terms of correlation coefficient (CC), the GPM IMERG products are better than Fengyun 2G. Among them, GPM IMERG Late Run is better than Early Run, and Final Run is better than Late Run; (2) In terms of root mean square error (RMSE) and mean absolute error (MAE), the values of GPM products are higher; (3) The GPM IMERG products show that the CC of the lower reaches is better than that of the upper reaches; (4) The RMSE and MAE are better in the upper reaches than in the lower reaches, and the high value of the error is mainly concentrated in the lower reaches. This study provides a more reliable precipitation data guarantee for the cascade management of Lancang River Basin, and has certain scientific significance and practical value.
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Dhupia, Bhawna, and M. Usha Rani. "Assessment of Electric Consumption Forecast Using Machine Learning and Deep Learning Models for the Industrial Sector." In Advances in Wireless Technologies and Telecommunication, 206–18. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7685-4.ch016.

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Power demand forecasting is one of the fields which is gaining popularity for researchers. Although machine learning models are being used for prediction in various fields, they need to upgrade to increase accuracy and stability. With the rapid development of AI technology, deep learning (DL) is being recommended by many authors in their studies. The core objective of the chapter is to employ the smart meter's data for energy forecasting in the industrial sector. In this chapter, the author will be implementing popular power demand forecasting models from machine learning and compare the results of the best-fitted machine learning (ML) model with a deep learning model, long short-term memory based on RNN (LSTM-RNN). RNN model has vanishing gradient issue, which slows down the training in the early layers of the network. LSTM-RNN is the advanced model which take care of vanishing gradient problem. The performance evaluation metric to compare the superiority of the model will be R2, mean square error (MSE), root means square error (RMSE), and mean absolute error (MAE).
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Musonge, P. "A Statistical Approach to Model Selection for Dynamic Adsorption Columns." In Advances in Wastewater Treatment II, 128–67. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901397-5.

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A variety of models have been used to describe and predict breakthrough curves for dynamic adsorption systems, in order to scale up laboratory and pilot plant systems. There are however limitations in the applicability of existing models. The study is aimed at providing unambiguous approaches in selecting the best performing model between Thomas, Yoon-Nelson and Bohart-Adams (B-A) models for three dynamic adsorption systems. Three approaches were implemented in this study using published experimental data of three adsorption systems. The first approach was the application of statistical analysis between actual and predicted breakthrough curves without modifying the models. The second and third approaches were application of local mean values (LMV) and global mean values (GMV) of empirical constants to predict breakthrough curves. Predictive and generalization performances of the three models were evaluated using the statistical criteria of Mean Absolute Error (MAE), Root mean Squared Error (RMSE) and Correlation Coefficient (R2).
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Conference papers on the topic "Mean absolute error (MAE)"

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Oliveira Junior, Adair da Silva, Marcio Carneiro Brito Pache, Fábio Prestes Cesar Rezende, Diego André Sant’Ana, Vanessa Aparecida de Moraes Weber, Gilberto Astolfi, Fabricio de Lima Weber, et al. "An Investigation of Parameter Optimization in Fingerling Counting Problems." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18881.

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The objective of this paper is to investigate which combination of parameters for the fingerling counting software results in the smallest Mean Absolute Error (MAE) and smallest Root Mean Squared Error (RMSE). For this, an image dataset called FISHCV155V was created and separated into training and test sets, where different combinations of parameters for the software were tested. From the obtained results were extracted individual performance metrics for each combination of parameters, such as MAE, Mean Square Error (MSE) and RMSE. Video frames were analysed comparing the parameter combination that obtained the best and worst results, in order to investigate the influence of such parameters in the performance of the software. From such results, it was concluded that the best combination reached 5.99 MAE and 9.96 RMSE.
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Alves, Emilly Pereira, Joao Fausto Lorenzato Oliveira, Manoel Henrique da Nóbrega Marinho, and Francisco Madeiro. "A Nonlinear Optimizated PSO-SVR Hybrid System for Time Series Forecasting with ARIMA." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-54.

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In the forecasting time series field, the combination of techniques to aid in predicting different patterns has been the subject of several studies. Hybrid models have been widely applied in this scenario, where the vast majority of series are composed of linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) presents satisfactory results in a linear pattern prediction but can not capture nonlinear ones. In dealing with nonlinear patterns, the Support Vector Regression (SVR) has shown promising results. In order to map both patterns, an optimized nonlinear combination model based on SVR and ARIMA is proposed. The main difference in comparison with other works is the use of an interactive Particle Swarm Optimization (PSO) to increase the prediction performance. To the experimental setup, six well-known datasets of the literature is used. The performance is assessed by the metrics Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The results show the proposed system attains better outcomes when compared to the other tested techniques, for most of the used data.
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Surgelas, Vladimir, Vivita Pukite, and Irina Arhipova. "Property evaluation based on ambiguous logic through building inspection in São Paulo city, Brazil." In Research for Rural Development 2021 : annual 27th International scientific conference proceedings. Latvia University of Life Sciences and Technologies, 2021. http://dx.doi.org/10.22616/rrd.27.2021.041.

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The civil engineering branch is strongly related to the development of the countries and there is still a lot of information available in the buildings constructed. However, these data are dispersed without proper treatment. On the other hand, if these real estate data are reorganized to discover behavior parameters, these properties’ values can be predicted and still work as data and causal relationships between explanatory variables. The purpose of the research is to use construction inspection strategies associated with artificial intelligence to predict the market value of a residential apartment. In this academic experiment, only 6 samples of residential apartments are used. Those samples are located in the Lithuania Republic square at Vila Zelina neighborhood, in São Paulo, Brazil, a source in February 2021. The method uses the results of the inspection of civil engineering and converts them into linguistic terms. The result considers the imprecision, uncertainty, and subjectivity of human expression combined with artificial intelligence and civil engineering. To test the feasibility of the process, a comparison is made between the market values of the samples and the values predicted by the Fuzzy logic. Thus, the good results derived a Main Percentage Absolute Error (MAPE) of 5%, the mean absolute error (MAE), root-mean-square error (RMSE), and determination coefficient (R2) of 0.99.
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ÜNEŞ, Fatih, Bestami TAŞAR, Hakan VARÇİN, and Ercan GEMİCİ. ""River Sediment Amounts Prediction with Regression and Support Vector Machine Methods."." In Air and Water – Components of the Environment 2022 Conference Proceedings. Casa Cărţii de Ştiinţă, 2022. http://dx.doi.org/10.24193/awc2022_10.

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Accurate estimation of the amount of sediment in rivers; determination of pollution, river transport, determination of dam life, etc. matters are very important. In this study, sediment estimation in the river was made using Interaction Regression (IR), Pure-Quadratic Regression (PQR) and Support Vector machine (SVM) methods. The observation station on the Patapsco River near Catonsville was chosen as the study area. Prediction model was developed by using daily flow and turbidity data between 2015- 2018 as input parameters. Models were compared to each other according to three statistical criteria, namely, root mean square errors (RMSE), mean absolute relative error (MAE) and determination coefficient (R2 ). These criteria were used to evaluate the performance of the models. When the model results were compared with each other, it was seen that the IR model gave results consistent with the actual measurement results.
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Milanovic, Mladen. "ANALYSIS OF DIFFERENT REFERENCE EVAPOTRANSPIRATION METHODS IN SOUTH-EASTERN SERBIA." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/3.1/s12.03.

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Evapotranspiration is an important component of hydrological balance, and it represents a complex process of water evaporation form water bodies, soil and plants. The Food and Agriculture Organization of the UN proposed the Penman-Monteith equation as the most reliable method for calculating reference evapotranspiration. However, the application of Penman-Monteith is often limited in many regions, due to the requirement for a large amount of meteorological data. In such circumstances, it is necessary to identify an alternative method with similar efficiency. The goal of the paper is to define the best method, out of the Hargreaves, adjusted Hargreaves, Thornthwaite, calibrated Thornthwaite and adjusted Thornthwaite methods, which gives results similar to those of the Penman-Monteith method (but does not require a wide range of input data) for South-eastern Serbia. South-eastern Serbia was observed through Nis, Vranje and Dimitrovgrad stations, for the period 1989-2018. In order to define the best match with the Penman�Monteith method, statistical indicators were used: mean bias error � MBE, mean absolute error � MAE and root mean square error � RMSE. The final results of the indicators showed that, from all the analyzed methods, the adjusted Hargreaves method matches the Penman-Monteith method best, i.e. the adjusted Hargreaves method has the following values: MBE = -0.076, MAE = 0.225 and RMSE = 0.318, which are the lowest values, compared to other methods.
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Yan, Chang, Shengjun Ju, Dilong Guo, Guowei Yang, and Shuanbao Yao. "Inferring Unsteady Wake Flow Fields From Partial Data by Physics-Informed Neural Networks." In ASME 2022 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/fedsm2022-86945.

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Abstract Massive differential numerical computations are necessary in Computational Fluid Dynamics. In addition, the experimental results are generally noisy. Consequently, traditional methods cannot get unsteady flow fields immediately and precisely. In this research, the inferences of unsteady wake flow fields at different Reynolds numbers by Physics-Informed Neural Networks (PINNs) are studied. Unlike typical neural networks whose loss function consists of Mean Square Error only, the loss function of PINNs consists of Mean Square Error and the sum of squares of residuals of the flow governing equations. The flow governing equations are introduced to the neural networks as a regularization of the loss function. The existence of regular term reduces the dependence on labeled data during training. Then the PINNs is trained with very little labeled data (5% of the full field). After being trained, the PINNs show excellent performance in inferring the unsteady wake flow fields. When the Reynolds number is 1e2, the Mean Absolute Error (MAE) of the reconstructed velocity field is on the order of 1e−4. Meanwhile, the MAE increases with the increase of Reynolds number. In addition, even if the random noise of the training set is introduced up to 20%, the result is still acceptable, which demonstrates the great anti-noise ability of physics-informed neural networks.
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Bielecki, Mark F., Jason J. Kemper, and Thomas L. Acker. "A Methodology for Comprehensive Characterization of Errors in Wind Power Forecasting." In ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90381.

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Wind power forecasting will play a more important role in electrical system planning with the greater wind penetrations of the coming decades. Wind will most likely comprise a larger percentage of the generation mix, and as a result forecasting errors may have more significant effects on balancing operations. The natural uncertainties associated with wind along with limitations in numerical weather prediction (NWP) models lead to these forecasting errors, which play a considerable role in the impacts and costs of utility-scale wind integration. The premise of this project was to examine errors between the actual and commercially forecasted power production data from a typical wind power plant in the Northwestern United States. An exhaustive statistical characterization of the forecast behavior and error trends was undertaken, which allowed the most important metrics for describing wind power forecast errors to be identified. This paper presents only the metrics considered by the authors to be most significant. While basic information about wind forecast accuracy such as the mean absolute error (MAE) is valuable, a more detailed description is useful for system operators or in wind integration studies. System planners have expressed major concern in the area of forecast performance during large wind ramping events. For such reasons, this methodology included the development of a comprehensive ramp identification algorithm to select significant ramp events from the data record, and particular attention was paid to the error analysis during these events. The algorithm allows user input to select ramps of any desired magnitude, and also performs correlation analysis between forecasted ramp events and actual ramp events that coincide within a desired timing window. From this procedure, an investigation of the magnitude and phase of forecast errors was conducted for various forecast horizons. The metrics found to be of most importance for error characterization were selected based on overall impacts, and were ranked in a rudimentary (and perhaps subjective) order of significance. These metrics included: mean absolute error, root mean square error, average magnitude of step changes, standard deviation of step changes, mean bias levels, correlation coefficient of power values, mean temporal bias of ramp events, and others. While these metrics were selected and the methodology was developed for a single dataset, the entire process can be applied generally to any wind power and forecast time series. The implications for such a process include use for generating a synthetic wind power forecast for wind integration studies that will reproduce the same error trends as those found in a real forecast.
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Tian, Ye, Wei Huang, Pengfei Li, Simin Cao, and Yan Sun. "Two Phase Boiling Heat Transfer of Water in Minichannel." In 2017 25th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/icone25-66552.

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Printed Circuit Heat Exchangers (PCHE) is a new type of compact heat exchangers, it will be widely used for nuclear industry due to its higher heat transfer area density, compact size, and design flexibility. The hydraulic diameter of PCHE tubes ranges from 1mm to 2mm which belongs to mini-channel according to Kandlikar and Grande (2003)’s study.[1] In this paper, two-phase flow boiling heat transfer of water in mini-channel is discussed. The most of previous literatures in this field mainly focused on flow boiling of refrigerants, but the main working fluid in PCHE tubes is water. A composite correlation of flow boiling of water through mini-channel has been developed on basis of a database of water in this paper. Mean absolute error (MAE) method is used to evaluate relative error. Comparing with the experimental data, the MAE of the new correlation is 23.4%.
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Okello, Job Lazarus, Ahmed M. R. Fath El-Bab, Masahiko Yoshino, Hassan A. El-Hofy, and Mohsen A. Hassan. "Modelling of Surface Roughness in CO2 Laser Ablation of Aluminium-Coated Polymethyl Methacrylate (PMMA) Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-92024.

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Abstract High surface roughness hinders the flow of fluids in microchannels leading to low accuracy and poor-quality products. In this work, the adaptive neuro-fuzzy inference system (ANFIS) was used to examine surface roughness in CO2 laser fabrication of microchannels on polymethyl methacrylate (PMMA). The PMMA substrates were coated with a 500 nm layer of 99.95% pure aluminium. The inputs were speed (10, 15, and 20 mm/s), power (1.5, 3.0, and 4.5 W), and pulse rate (800, 900, and 1000 pules per inch) while the output was surface roughness. A 3-level full factorial design of experiments was used, and 27 experiments were conducted. Using the gaussian membership function (gaussmf), the ANFIS model was developed using the ANFIS toolbox in MATLAB R2022a. Analysis of variance was performed to examine the significance of the inputs. Power is the most significant followed by speed and pulse rate. The mean relative error (MRE), mean absolute error (MAE), and the correlation coefficient (R) were used to examine the accuracy and viability of the model. MRE, MAE, and R were found to be 0.257, 0.899, and 0.9957 (R2 = 0.9914) respectively. The root mean square error (RMSE) was 0.0022 and 3.6099 for the training data and checking data respectively. Hence, the developed model can predict the values of the average surface roughness with high accuracy.
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Kwok, Chun K., Matthew M. Asada, Jonathan R. Mita, and Weilin Qu. "Experimental and Numerical Study of Methanol-Water Mixture Single-Phase Heat Transfer in a Micro-Channel Heat Sink." In ASME 2012 Heat Transfer Summer Conference collocated with the ASME 2012 Fluids Engineering Division Summer Meeting and the ASME 2012 10th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/ht2012-58477.

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This paper presents an experimental study of single-phase heat transfer characteristics of binary methanol-water mixtures in a micro-channel heat sink containing an array of 22 microchannels with 240μm × 630μm cross-section. Pure water, pure methanol, and five methanol-water mixtures with methanol molar fraction of 16%, 36%, 50%, 63% and 82% were tested. Key parametric trends were identified and discussed. The experimental study was complemented by a three-dimensional numerical simulation. Numerical predictions and experimental data are in good agreement with a mean absolute error (MAE) of 0.87%.
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Reports on the topic "Mean absolute error (MAE)"

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Hodgdon, Taylor, Anthony Fuentes, Brian Quinn, Bruce Elder, and Sally Shoop. Characterizing snow surface properties using airborne hyperspectral imagery for autonomous winter mobility. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42189.

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With changing conditions in northern climates it is crucial for the United States to have assured mobility in these high-latitude regions. Winter terrain conditions adversely affect vehicle mobility and, as such, they must be accurately characterized to ensure mission success. Previous studies have attempted to remotely characterize snow properties using varied sensors. However, these studies have primarily used satellite-based products that provide coarse spatial and temporal resolution, which is unsuitable for autonomous mobility. Our work employs the use of an Unmanned Aeriel Vehicle (UAV) mounted hyperspectral camera in tandem with machine learning frameworks to predict snow surface properties at finer scales. Several machine learning models were trained using hyperspectral imagery in tandem with in-situ snow measurements. The results indicate that random forest and k-nearest neighbors models had the lowest Mean Absolute Error for all surface snow properties. A pearson correlation matrix showed that density, grain size, and moisture content all had a significant positive correlation to one another. Mechanically, density and grain size had a slightly positive correlation to compressive strength, while moisture had a much weaker negative correlation. This work provides preliminary insight into the efficacy of using hyperspectral imagery for characterizing snow properties for autonomous vehicle mobility.
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