Journal articles: '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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Zamhuri Fuadi, Azam, Irsyad Nashirul Haq, and Edi Leksono. "Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 3 (June 19, 2021): 466–73. http://dx.doi.org/10.29207/resti.v5i3.2947.

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Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimal parameter values C 1e6 and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14.

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Estrada, Ma del Rocío Castillo, Marco Edgar Gómez Camarillo, María Eva Sánchez Parraguirre, Marco Edgar Gómez Castillo, Efraín Meneses Juárez, and M. Javier Cruz Gómez. "Evaluation of Several Error Measures Applied to the Sales Forecast System of Chemicals Supply Enterprises." International Journal of Business Administration 11, no. 4 (June 30, 2020): 39. http://dx.doi.org/10.5430/ijba.v11n4p39.

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The objective of the industry in general, and of the chemical industry in particular, is to satisfy consumer demand for products and the best way to satisfy it is to forecast future sales and plan its operations.Considering that the choice of the best sales forecast model will largely depend on the accuracy of the selected indicator (Tofallis, 2015), in this work, seven techniques are compared, in order to select the most appropriate, for quantifying the error presented by the sales forecast models. These error evaluation techniques are: Mean Percentage Error (MPE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Symmetric Mean Absolute Percentage Error (SMAPE) and Mean Absolute Arctangent Percentage Error (MAAPE). Forecasts for chemical product sales, to which error evaluation techniques are applied, are those obtained and reported by Castillo, et. al. (2016 & 2020).The error measuring techniques whose calculation yields adequate and convenient results, for the six prediction techniques handled in this article, as long as its interpretation is intuitive, are SMAPE and MAAPE. In this case, the most adequate technique to measure the error presented by the sales prediction system turned out to be SMAPE.

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Huda, Didik Nur, and Santy Handayani. "Prediksi Nilai Ujian dengan Artificial Neural Network." remik 7, no. 1 (January 1, 2023): 157–65. http://dx.doi.org/10.33395/remik.v7i1.11983.

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Artificial Neural Network (ANN) dalam pendidikan lebih sering digunakan untuk klasifikasi, akan tetapi ANN dapat digunakan untuk memprediksi suatu nilai. Nilai dari hasil belajar mata kuliah fisika listrik magnet dapat dimodelkan dengan ANN ini. Dataset yang diperoleh dari nilai yang ada di classroom dan quizizz. Dataset setelah diproses terdiri dari 19 fitur (variabel) dan 1 output nilai yang berisi 113 baris. Dataset dibagi menjadi 80% untuk melatih model dan 20% untuk uji coba model. Dalam model ANN untuk memprediksi nilai ini, pendekatan yang digunakan adalah Mean Squared Error (MSE) dan Mean Absolute Error (MAE). Pendekatan MAE lebih baik dibandingkan MSE, karena selisih dengan nilai sebenarnya tidak terlalu jauh. Sedangkan dari hasil akurasi dinyatakan menggunakan rerata Absolute Percent Error (APE). Hasil akurasi pendekatan MSE dan MAE yaitu 88,97% dan 89,99%.

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Yogafanny, Ekha, and Djoko Legono. "A COMPARATIVE STUDY OF MISSING RAINFALL DATA ANALYSIS USING THE METHODS OF INVERSED SQUARE DISTANCE AND ARITHMETIC MEAN." ASEAN Engineering Journal 12, no. 2 (June 1, 2022): 69–74. http://dx.doi.org/10.11113/aej.v12.16974.

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In water resources planning and management, it is essential to have reliable rainfall data. In many cases, rainfall data under the guardian national/ local institution are incomplete. Some data are missing, both monthly and annually. The missing data may persist due to neither damage nor human error. This study aims to estimate the missing rainfall data using two methods, i.e., the inverse square distance and the arithmetic mean methods. The study compared the two mentioned methods using root mean square error (RMSE) and mean absolute error (MAE) and to determine the consistency of rainfall data in all stations using double mass curve analysis. This study utilized the rainfall data from Tepus, Semanu, Rongkop, and Tanjungsari Stations in Gunung Kidul Regency, Yogyakarta Province, Indonesia. The model performance was tested by the root mean square error (RMSE) and mean absolute error (MAE). The rainfall data consistency was determined by double mass curve analysis. The results showed that the arithmetic mean method performed better rather than the inverse square distance method. The smallest RMSE and MAE values in the arithmetic method at the four stations have confirmed the statement. The rainfall data consistency analyzed by the double mass curve is consistent in all stations except Tepus Station.

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Aijaz, Iflah, and Parul Agarwal. "A Study on Time Series Forecasting using Hybridization of Time Series Models and Neural Networks." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 827–32. http://dx.doi.org/10.2174/1573401315666190619112842.

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Introduction: Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) are leading linear and non-linear models in Machine learning respectively for time series forecasting. Objective: This survey paper presents a review of recent advances in the area of Machine Learning techniques and artificial intelligence used for forecasting different events. Methods: This paper presents an extensive survey of work done in the field of Machine Learning where hybrid models for are compared to the basic models for forecasting on the basis of error parameters like Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Normalized Root Mean Square Error (NRMSE). Results: Table 1 summarizes important papers discussed in this paper on the basis of some parameters which explain the efficiency of hybrid models or when the model is used in isolation. Conclusion: The hybrid model has realized accurate results as compared when the models were used in isolation yet some research papers argue that hybrids cannot always outperform individual models.

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Wójcik, Filip, and Michał Górnik. "Improvement of e-commerce recommendation systems with deep hybrid collaborative filtering with content: A case study." Econometrics 24, no. 3 (2020): 37–50. http://dx.doi.org/10.15611/eada.2020.3.03.

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This paper presents a proposition to utilize flexible neural network architecture called Deep Hybrid Collaborative Filtering with Content (DHCF) as a product recommendation engine. Its main goal is to provide better shopping suggestions for customers on the e-commerce platform. The system was tested on 2018 Amazon Reviews Dataset, using repeated cross validation and compared with other approaches: collaborative filtering (CF) and deep collaborative filtering (DCF) in terms of mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). DCF and DHCF were proved to be significantly better than the CF. DHCF proved to be better than DCF in terms of MAE and MAPE, it also scored the best on separate test data. The significance of the differences was checked by means of a Friedman test, followed by post-hoc comparisons to control p-value. The experiment shows that DHCF can outperform other approaches considered in the study, with more robust scores

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Liu, Yongzhi, Wenting Zhang, Ying Yan, Zhixuan Li, Yulin Xia, and Shuhong Song. "An Effective Rainfall–Ponding Multi-Step Prediction Model Based on LSTM for Urban Waterlogging Points." Applied Sciences 12, no. 23 (December 2, 2022): 12334. http://dx.doi.org/10.3390/app122312334.

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With the change in global climate and environment, the prevalence of extreme rainstorms and flood disasters has increased, causing serious economic and property losses. Therefore, accurate and rapid prediction of waterlogging has become an urgent problem to be solved. In this study, Jianye District in Nanjing City of China is taken as the study area. The time series data recorded by rainfall stations and ponding monitoring stations from January 2015 to August 2018 are used to build a ponding prediction model based on the long short-term memory (LSTM) neural network. MSE (mean square error), MAE (mean absolute error) and MSLE (mean squared logarithmic error) were used as loss functions to conduct and train the LSTM model, then three ponding prediction models were built, namely LSTM (mse), LSTM (mae) and LSTM (msle), and a multi-step model was used to predict the depth of ponding in the next 1 h. Using the measured ponding data to evaluate the model prediction results, we selected rmse (root mean squared error), mae, mape (mean absolute percentage error) and NSE (Nash–Sutcliffe efficiency coefficient) as the evaluation indicators. The results showed that LSTM (msle) was the best model among the three models, with evaluation indicators as follows: rmse 5.34, mae 3.45, mape 53.93% and NSE 0.35. At the same time, we found that LSTM (mae) has a better prediction effect than the LSTM (mse) and LSTM (msle) models when the ponding depth exceeds 30 mm.

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Ellahi, Manzoor, Muhammad Rehan Usman, Waqas Arif, Hafiz Fuad Usman, Waheed A. Khan, Gandeva Bayu Satrya, Kamran Daniel, and Noman Shabbir. "Forecasting of Wind Speed and Power through FFNN and CFNN Using HPSOBA and MHPSO-BAACs Techniques." Electronics 11, no. 24 (December 15, 2022): 4193. http://dx.doi.org/10.3390/electronics11244193.

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Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed and power using the implementation of the Feedforward and cascaded forward neural networks (FFNNs and CFNNs, respectively). The one and half year’s dataset for Jhimpir, Pakistan, is used to train FFNNs and CFNNs with recently developed novel metaheuristic optimization algorithms, i.e., hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) named HPSOBA, along with a modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), without and with the constriction factor (MHPSO-BAAC-χ). The forecasting results are made for June–October 2019. The accuracy of the forecasted values is tested through the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The graphical and numerical comparative analysis was performed for both feedforward and cascaded forward neural networks that are tuned using the mentioned optimization techniques. The feedforward neural network was achieved through the implementation of HPSOBA with a mean absolute error, mean absolute percentage error, and root mean square error of 0.0673, 6.73%, and 0.0378, respectively. Whereas for the case of forecasting through a cascaded forward neural network, the best performance was attained by the implementation of MHPSO-BAAC with a MAE, MAPE and RMSE of 0.0112, 1.12%, and 0.0577, respectively. Thus, the mentioned neural networks provide a more accurate prediction when trained and tuned through the given optimization algorithms, which is evident from the presented results.

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Ahmad, Ayaz, Furqan Farooq, Pawel Niewiadomski, Krzysztof Ostrowski, Arslan Akbar, Fahid Aslam, and Rayed Alyousef. "Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm." Materials 14, no. 4 (February 8, 2021): 794. http://dx.doi.org/10.3390/ma14040794.

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Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

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Pandher, Sharandeep Singh, Arzu Sardarli, and Andrei Volodin. "Forecasting of Immigrants in Canada using Forecasting models." Journal of Probability and Statistical Science 20, no. 1 (October 3, 2022): 98–107. http://dx.doi.org/10.37119/jpss2022.v20i1.511.

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In Canada, the number of international students, temporary workers and refugees from every part of the world grows each year. Therefore, forecasting immigration is important for the economy of Canada and Labor Market. In this regard, four forecasting approaches have been applied to the annual data of immigrants for the period 2000-2019. The accuracy of Moving average (MA), Autoregressive (AR), Autoregressive moving average (ARMA), Autoregressive integrated moving average (ARIMA) models were checked via comparing Akaike’s information criteria(AIC), Bayesian information criteria (BIC), Mean error (ME), Root mean square error(RMSE), Mean absolute error (MAE), Mean percentage error (MPE), Mean absolute percentage error (MAPE) and Mean absolute scaled error (MASE) and graphical approaches such as ACF plots of residuals. Experimental results showed that ARIMA (1,2,4) is the best-fitted model for forecasting immigrants in Canada. Selected forecasting approaches are applied to predict immigrants for five years from 2020-2024.

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Emir, Senol. "Predicting the Istanbul Stock Exchange Index Return using Technical Indicators." International Journal of Finance & Banking Studies (2147-4486) 2, no. 3 (July 21, 2013): 111–17. http://dx.doi.org/10.20525/ijfbs.v2i3.158.

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The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.

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Pekel, Engin, Muhammet Gul, Erkan Celik, and Samuel Yousefi. "Metaheuristic Approaches Integrated with ANN in Forecasting Daily Emergency Department Visits." Mathematical Problems in Engineering 2021 (November 27, 2021): 1–14. http://dx.doi.org/10.1155/2021/9990906.

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The overall service quality level of Emergency Departments (EDs) can be improved by accurate forecasting of patient visits. Accordingly, this study aims to evaluate the use of three metaheuristic approaches integrated with Artificial Neural Network (ANN) in forecasting daily ED visits. To do this, five performance measures are used for evaluating the accuracy of the proposed approaches, including Bayesian ANN, Genetic Algorithm-based ANN (GA-ANN), and Particle Swarm Optimization algorithm-based ANN (PSO-ANN). The outputs of this study show that the PSO-ANN model provides the most dominant performance in both the training and testing process. The lowest error is obtained with a mean absolute percentage error (MAPE) of 6.3%, Mean Absolute Error (MAE) of 42.797, Mean Squared Error (MSE) of 2499.340, Root Mean Square Error (RMSE) of 49.933, and R-squared (R2) of 0.824 on the training dataset. The lowest error with an MAPE of 6.0%, MAE of 40.888, MSE of 2839.998, RMSE of 53.292, and R2 of 0.791 is also obtained on the testing process.

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Dimitriadou, Stavroula, and Konstantinos G. Nikolakopoulos. "Development of the Statistical Errors Raster Toolbox with Six Automated Models for Raster Analysis in GIS Environments." Remote Sensing 14, no. 21 (October 29, 2022): 5446. http://dx.doi.org/10.3390/rs14215446.

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The Statistical Errors Raster Toolbox includes models of the most popular error metrics in the interdisciplinary literature, namely, root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), normalized mean bias error (NMBE), mean absolute error (MAE) and normalized mean absolute error (NMAE), for computing the areal errors of any raster file in .tiff format as compared with a reference raster file. The models are applicable to any size of raster files, no matter if no-data pixels are included. The only prerequisites are that the two raster files share the same units, cell size, and projection system. The novelty lies in the fact that, to date, there is no such application in ArcGIS Pro 3/ArcMap 10.8. Therefore, users who work with raster files require external software, plus the relevant expertise. An application on the reference evapotranspiration (ETo) of Peloponnese peninsula (Greece) is presented. MODIS ET products and ETo raster files for empirical methods are employed. The results of the models (for 20,440 valid values) are compared to the results of external software (for 1000 random points). Considering that the different sample sizes can lead to different accuracies and the inhomogeneity of the area, it is obvious that the results are almost identical.

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Adam Haggagy, Mahmoud El-Nouby, Abdel Galeil Abdel Elal Hassan, Badry Noby Mohamed Abd Allah, and Ezzat Ramadan Mahmoud. "Calculation of the Global Solar Radiation in a Subtropical Region (Qena, Egypt)." International Journal of Research Publication and Reviews 04, no. 01 (2022): 1880–84. http://dx.doi.org/10.55248/gengpi.2023.4153.

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Air temperature (T, °C), relative humidity (RH, %), and global solar radiation (G, MJ.m-2 ) have been measured in the meteorological station at South Valley University (SVU) at Qena, Egypt, from 2000 to 2013, while the total column ozone (TCO, DU) is downloaded from Giovanni's website. T, RH, and TCO are important meteorological parameters, and they are useful to estimate the missing data of the global solar radiation (G), as global solar radiation is desirable for electricity generation applications and for agriculture. Qena is a subtropical region in Upper Egypt, as it's characterized by clear weather most days of the year, and it's very hot in the summer and cold in the winter. The meteorological station at South Valley University (SVU) stopped measuring the G. The linear regression equation and the most important statistical indices are used in this paper such as, the determination coefficient (R2 ), the mean absolute error (MAE), the mean absolute bias error (MABE), the mean square error (MSE), the root mean square error (RMSE), the mean percentage error (MPE), the mean bias error (MBE), the model efficiency (ME), and the agreement index (d). For verification of the empirical models' efficiency, the data of a new period has used, 2013, and the results of the models were excellent and valid for estimating the missing data, as R2 was more than 0.92 in all models but it was near one in models 1 and 4. MAE was close to zero for all models. MBE, MPE, and MABE were close to zero for all models except model 3. Model 1 was the best one, as, R2 , MAE, MABE, MSE, RMSE, MAPE, d, ME, and MPE were 0.9883, 0.0193, 0.383, 0.2303, 0.4799, 1.927, 0.9979, 0.992, and 0.2932, respectively

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Lee, Jui-Che, Jeng-Da Chai, and Shiang-Tai Lin. "Assessment of density functional methods for exciton binding energies and related optoelectronic properties." RSC Advances 5, no. 123 (2015): 101370–76. http://dx.doi.org/10.1039/c5ra20085g.

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Ican, Özgür, and Taha Bugra Çelik. "Stock Market Prediction Performance of Neural Networks: A Literature Review." International Journal of Economics and Finance 9, no. 11 (October 15, 2017): 100. http://dx.doi.org/10.5539/ijef.v9n11p100.

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In this paper, previous studies featuring an artificial neural networks based prediction model have been reviewed. The main purpose of this review is to examine studies which use directional prediction accuracy (also known as hit ratio) or profitability of the model as a benchmark since other forecast error measures - namely mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE) and mean squared error (MSE) - have been criticized for the argument that they are not able to actually show how useful the prediction model is, in terms of financial gains (i.e. for practical usage). In order to meet the publication selection criteria mentioned above, a large number of publications have been examined and 25 of papers satisfying the criteria are selected for comparison. Classification of the eligible papers are summarized in a table format for future studies.

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Jasni, Nur Hazwani, Aida Mustapha, Siti Solehah Tenah, Salama A. Mostafa, and Nazim Razali. "Prediction of player position for talent identification in association netball: a regression-based approach." International Journal of Advances in Intelligent Informatics 8, no. 1 (March 31, 2022): 84. http://dx.doi.org/10.26555/ijain.v8i1.707.

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Among the challenges in industrial revolutions, 4.0 is managing organizations’ talents, especially to ensure the right person for the position can be selected. This study is set to introduce a predictive approach for talent identification in the sport of netball using individual player qualities in terms of physical fitness, mental capacity, and technical skills. A data mining approach is proposed using three data mining algorithms, which are Decision Tree (DT), Neural Network (NN), and Linear Regressions (LR). All the models are then compared based on the Relative Absolute Error (RAE), Mean Absolute Error (MAE), Relative Square Error (RSE), Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Relative Square Error (RSE). The findings are presented and discussed in light of early talent spotting and selection. Generally, LR has the best performance in terms of MAE and RMSE as it has the lowest values among the three models.

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Ramly, Nurfarawahida, Mohd Saifullah Rusiman, Norziha Che Him, Maria Elena Nor, Supar Man, NurAin Zafirah Ahmad Basri, and Nazeera Mohamad. "A new hybrid of Fuzzy C-Means Method and Fuzzy Linear Regression Model in Predicting Manufacturing Income." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 473. http://dx.doi.org/10.14419/ijet.v7i4.30.22371.

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Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with first. Thus, fuzzy structure system is considered. The objectives of this study are to determine suitable cluster by using fuzzy c-means (FCM) method, to apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni and to improve the FCM method and FLR model proposed by Zolfaghari to predict manufacturing income. This study focused on FLR which is suitable for ambiguous data in modelling. Clustering is used to cluster or group the data according to its similarity where FCM is the best method. The performance of models will measure by using the mean square error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE). Results shows that the improvisation of FCM method and FLR model obtained the lowest value of error measurement with MSE=1.825 , MAE=115932.702 and MAPE=95.0366. Therefore, as the conclusion, a new hybrid of FCM method and FLR model are the best model for predicting manufacturing income compared to the other models.

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Geevaretnam, Jothi Letchumy, Norziha Megat Mohd. Zainuddin, Norshaliza Kamaruddin, Hazlifah Rusli, Nurazean Maarop, and Wan Azlan Wan Hassan. "Predicting the Carbon Dioxide Emissions Using Machine Learning." International Journal of Innovative Computing 12, no. 2 (November 20, 2022): 17–23. http://dx.doi.org/10.11113/ijic.v12n2.369.

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There are severe impacts and consequences to humans, societies, and the environment due to global warming. Though there are various activities that contributes to global warming, the major contributor is carbon dioxide (CO2) emissions. Human activities release large amounts of carbon dioxide from the burning of fossil fuels, such as oil, gas, or coal in producing energy. Net zero is the new ambition of industries in balancing the CO2 emissions in environment. Thus, this study finds the best predictive model for CO2 emissions using machine learning model with the dataset of CO2 emissions from 1991 until 2020. Machine Learning techniques is an efficient approach to study the CO2 emissions prediction and has been very appealing to few research. The dataset is split into a train-test (estimation-validation) set with 80% train set and 20% test set (80:20) proportion. The predictive model was developed using Random Forest, Support Vector Machine and Artificial Neural Network algorithms with different parameters to get the outcome. The predictive model's performance was evaluated based on the error measurement metric of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Its reveals that Support Vector Machine with linear kernel function is the best model among others which produces 65.7254 Mean Absolute Error (MAE), 112.2196 Root Mean Square Error (RMSE) and 0.2279% Mean Absolute Percentage Error (MAPE) from the train set. For industries committed to net zero carbon emissions, this analysis will be an advising factor on the prediction system to find the CO2 emissions and how much fossil fuels’ reduction is required in achieving net zero carbon emission by 2050.

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Abdullah, Siti Nabilah Syuhada, Ani Shabri, and Ruhaidah Samsudin. "Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price." MATEMATIKA 35, no. 4 (December 31, 2019): 53–64. http://dx.doi.org/10.11113/matematika.v35.n4.1263.

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Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

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Sun, Shuo, Qianli Zhang, Junzhong Sun, Wei Cai, Zhiyong Zhou, Zhanlu Yang, and Zongliang Wang. "Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm." Energies 15, no. 16 (August 11, 2022): 5842. http://dx.doi.org/10.3390/en15165842.

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Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model.

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Mugume, Isaac, Charles Basalirwa, Daniel Waiswa, Joachim Reuder, Michel d. S. Mesquita, Sulin Tao, and Triphonia J. Ngailo. "Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model." Modelling and Simulation in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7530759.

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Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE), the mean absolute error (MAE), mean error (ME), skewness, and the bias easy estimate (BES)) and nonparametric (the sign test, STM) methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves) show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.

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Shafii, Nor Hayati Binti, Rohana Alias, Nur Fithrinnissaa Zamani, and Nur Fatihah Fauzi. "Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)." Journal of Computing Research and Innovation 5, no. 3 (October 21, 2020): 43–53. http://dx.doi.org/10.24191/jcrinn.v5i3.149.

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Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development. Accessing the air pollution index (API) accurately is very important to control its impact on environmental and human health. The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM). The data used is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah.

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Aqeel Khan, Mohd, and Manju Suthar. "Assessing the Results of Compressive Strength of Ultra High-Performance Concrete Using Soft Computing." IOP Conference Series: Earth and Environmental Science 1110, no. 1 (February 1, 2023): 012089. http://dx.doi.org/10.1088/1755-1315/1110/1/012089.

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Abstract Ultra-High-Performance Concrete (UHPC) is a new type of concrete that has gained popularity in recent decades due to its high strength and durability. In this study results of M5P and random forest are compared to predict the compressive strength of ultra-high-performance concrete for 28 days. A total of 236 readings are taken in this investigation. Out of 236 readings 70% that is 157 readings are used in training period and the remaining 79 are used for testing period. The results of Random Forest and M5P are then compare to find which is more effective in predicting the compressive strength of Ultra High-Performance concrete. The accuracy of the model depends upon Performance evaluation parameters which are Corelation Coefficient (CC), Root mean square error (RMSE) and mean absolute error (MAE). After getting the value of coefficient of correlation, root mean square error, mean absolute error it is observed that that Random Forest is better than M5P as the values of CC, RMSE, MAE are 0.8568, 16.005 and 12.03 for testing stage respectively.

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Huang, Chao, Longpeng Cao, Nanxin Peng, Sijia Li, Jing Zhang, Long Wang, Xiong Luo, and Jenq-Haur Wang. "Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception." Sustainability 10, no. 12 (December 19, 2018): 4863. http://dx.doi.org/10.3390/su10124863.

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Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

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Edupuganti, Sirisha, Ravichandra Potumarthi, Thadikamala Sathish, and Lakshmi Narasu Mangamoori. "Role of Feed Forward Neural Networks Coupled with Genetic Algorithm in Capitalizing of Intracellular Alpha-Galactosidase Production byAcinetobactersp." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/361732.

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Alpha-galactosidase production in submerged fermentation byAcinetobactersp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks was further analysed by using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), andR2-value for training and testing data. Using hybrid neural networks and genetic algorithm, alpha-galactosidase production was improved from 7.5 U/mL to 10.2 U/mL.

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Betti, Tihomir, Ivana Zulim, Slavica Brkić, and Blanka Tuka. "A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia." International Journal of Photoenergy 2020 (February 27, 2020): 1–14. http://dx.doi.org/10.1155/2020/9605950.

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The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

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Effendy, Nazrul, Eko David Kurniawan, Kenny Dwiantoro, Agus Arif, and Nidlom Muddin. "The prediction of the oxygen content of the flue gas in a gas-fired boiler system using neural networks and random forest." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (September 1, 2022): 923. http://dx.doi.org/10.11591/ijai.v11.i3.pp923-929.

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<p><span lang="EN-US">The oxygen content of the gas-fired boiler flue gas is used to monitor boiler combustion efficiency. Conventionally, this oxygen content is measured using an oxygen content sensor. However, because it operates in extreme conditions, this oxygen sensor tends to have the disadvantage of high maintenance costs. In addition, the absence of other sensors as an element of redundancy and when there is damage to the sensor causes manual handling by workers. It is dangerous for these workers, considering environmental conditions with high-risk hazards. We propose an artificial neural network (ANN) and random forest-based soft sensor to predict the oxygen content to overcome the problems. The prediction is made by utilizing measured data on the power plant’s boiler, consisting of 19 process variables from a distributed control system. The research has proved that the proposed soft sensor successfully predicts the oxygen content. Research using random forest shows better performance results than ANN. The random forest prediction errors are mean absolute error (MAE) of 0.0486, mean squared error (MSE) of 0.0052, root-mean-square error (RMSE) of 0.0718, and Std Error of 0.0719. While the errors using ANN are MAE of 0.0715, MSE of 0.0087, RMSE of 0.0935, and Std Error of 0.0935.</span></p>

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Kim, Cho Hwe, and Young Chul Kim. "Application of Artificial Neural Network Over Nickel-Based Catalyst for Combined Steam-Carbon Dioxide of Methane Reforming (CSDRM)." Journal of Nanoscience and Nanotechnology 20, no. 9 (September 1, 2020): 5716–19. http://dx.doi.org/10.1166/jnn.2020.17627.

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The application of artificial neural network (ANN) for modeling, combined steam-carbon dioxide reforming of methane over nickel-based catalysts, was investigated. The artificial neural network model consisted of a 3-layer feed forward network, with hyperbolic tangent function. The number of hidden neurons is optimized by minimization of mean square error and maximization of R2 (R square, coefficient of determination) and set of 8 neurons. With feed ratio, flow rate, and temperature as independent variables, methane, carbon dioxide conversion, and H2/CO ratio, were measured using artificial neural network. Coefficient of determination (R2) values of 0.9997, 0.9962, and 0.9985 obtained, and MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error) showed low value. This study indicates ANN can successfully model a highly nonlinear process and function.

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Afzal, Asif, Saad Alshahrani, Abdulrahman Alrobaian, Abdulrajak Buradi, and Sher Afghan Khan. "Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms." Energies 14, no. 21 (November 3, 2021): 7254. http://dx.doi.org/10.3390/en14217254.

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This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.

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Xu, Zhengqian, and Youchao Sun. "Study on Aircraft Cockpit Function Based on Neural Network." Wireless Communications and Mobile Computing 2022 (August 13, 2022): 1–8. http://dx.doi.org/10.1155/2022/7794982.

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With the rapid development of numerous airlines, various trend designs have been implemented to develop cockpit interiors for pilots. When communicating with most pilots, they express their emotional feelings about the cockpit. The performance-enhanced cockpit provides the pilot with a better emotional experience, further enhancing the comfort and pleasure of driving. Therefore, it is necessary to provide a cockpit with good performance. Therefore, this paper proposes a novel Neural Network-based Balanced Optimization Algorithm (NN-EOA) for cockpit emotion recognition. The proposed NN-EOA technique simulates quantitative computation with high accuracy and minimizes the error rate during evaluation. Here, images of the interior cockpit design were assessed on four emotional terms, namely, user-friendly (interactive), precise (precise), traditional (traditional), and neat (tidy). Finally, experimental results are performed on various parameters, namely, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) for various techniques. From the experimental evaluations, it can be seen that the proposed NN-EOA provides high agreement rates in the experimental group with the smallest MAE, MAPE, and RMSE.

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Mohapatra, Sabyasachi, Rohan Mukherjee, Arindam Roy, Anirban Sengupta, and Amit Puniyani. "Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?" Journal of Risk and Financial Management 15, no. 8 (August 7, 2022): 350. http://dx.doi.org/10.3390/jrfm15080350.

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This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and Turnover. Various error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root-Mean-Squared-Error (RMSE) have been used to check the performance of the models. Results show that the XGBoost algorithm performs best among the four ensemble models. The mean of absolute error and the root-mean-square -error vary around 3–5%. The feature importance plots generated by the models depict the importance of the variables in predicting the output. The proposed machine learning models help traders, investors, as well as portfolio managers, better predict the stock market trends and, in turn, the returns, particularly in banking stocks minimizing their sole dependency on macroeconomic factors. The techniques further assist the market participants in pre-empting any price-volume action across stocks irrespective of their size, liquidity, or past turnover. Finally, the techniques are incredibly robust and display a strong capability in predicting trend forecasts, particularly with any large deviations.

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Chen, Honggen, Xin Li, Yanqi Wu, Le Zuo, Mengjie Lu, and Yisong Zhou. "Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms." Buildings 12, no. 3 (March 4, 2022): 302. http://dx.doi.org/10.3390/buildings12030302.

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Compressive strength is an important mechanical property of high-strength concrete (HSC), but testing methods are usually uneconomical, time-consuming, and labor-intensive. To this end, in this paper, a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse aggregate, and superplasticizer. The prediction results were compared with those of the conventional support vector regression (SVR) model using four metrics, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R2). The results showed that the prediction accuracy and reliability of LSTM were higher with R2 = 0.997, RMSE = 0.508, MAE = 0.08, and MAPE = 0.653 compared to the evaluation metrics R2 = 0.973, RMSE = 1.595, MAE = 0.312, MAPE = 2.469 of the SVR model. The LSTM model is recommended for the pre-estimation of HSC compressive strength under a given mix ratio before the laboratory compression test. Additionally, the Shapley additive explanations (SHAP)-based approach was performed to analyze the relative importance and contribution of the input variables to the output compressive strength.

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Kumar, M. Sandeep, and Prabhu J. "Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means." International Journal of Web Portals 11, no. 2 (July 2019): 1–13. http://dx.doi.org/10.4018/ijwp.2019070101.

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In the era of Big Data, extremely complicated data is delivered from the system, of which it is impossible to collect the correct information with an online platform. In this research work, it provides a hybrid model for a movie-based recommender system; based on meta-heuristic firefly algorithm and fuzzy c-means (FCM) clustering technique to evaluate rating of a movie for a specific user based on the similarity of users and historical data. The firefly algorithm was employed in the movie lens dataset to get the initial cluster and also to initialize the position of clusters. FCM is used to classify the similarity of the user ratings. The proposed collaborative recommender system performed well regarding accuracy and precision. Various metrics are used in a movie lens dataset like mean absolute error (MAE), precision, and recall. The experimental result delivered by the system provides more efficient performance compared to the existing system in term of mean absolute error (MAE).

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Patil, Preethi, Jyothirani S. A., and Haragopal V. V. "Impact of Lockdown on India's Index of Industrial Production – Traditional and Deep Learning Statistical Approach." European Journal of Mathematics and Statistics 3, no. 4 (August 29, 2022): 62–70. http://dx.doi.org/10.24018/ejmath.2022.3.4.124.

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Index of Industrial Production (IIP) data is one of the important economic indicators that track the manufacturing activity of different sectors of an economy. In this paper, an attempt is made to forecast the IIP data using traditional and deep learning statistical approaches. The data from Apr-2012 to Feb-2020 is used for forecasting. The appropriate best model is evaluated by comparing mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results of the study show that RNN is performing better than the other models i.e ARIMA (Traditional method), FFNN, and LSTM (ANN models). Therefore RNN model is used for forecasting. The forecasted values from Mar-2020 to Jun-2021 are compared with the actual IIP values and resulted in a clear decline in industrial production because of lockdown.

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Prapcoyo, Hari. "PERAMALAN JUMLAH MAHASISWA MENGGUNAKAN MOVING AVERAGE." Telematika 15, no. 1 (April 30, 2018): 67. http://dx.doi.org/10.31315/telematika.v15i1.3069.

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AbstractThe Process of using resources in higher education is influenced by the up and down of the number students. The purpose of this study is to predict the number of students who study in the department of informatics engineering UPN Veteran Yogyakarta for the next periods. This research, data is taken from forlap dikti for Informatics Engineering fom 2009 until 2016 at UPN Veteran Yogyakarta. The method that used to forecast the number of students is a Moving Average method consisting of: Single Moving Average (SMA), Weighted Moving Average (WMA) and Exponential Moving Average (EMA). This study will use the forecasting accuracy namely Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) to select the best model to be used for forecasting. The best model that used for forecasting is Weighted Moving Average (WMA) with weighted 1/3 and average length (n) used for 2. The smallest value for MSE of 5807.96; the smallest MAE value of 55.89 and the smallest value for MAPE of 5.24%. Forecasting of the number of students for four semesters in the future after the even semester of 2016 are respectively: 902; 901,33; 901,56 and 901,48. Keywords : Forecasting, UPN Veteran Yogyakarta, Single moving average(SMA) AbstrakProses penggunaan sumber daya perguruan tinggi setiap tahun dipengaruhi oleh naik turunnya jumlah mahasiswa. Tujuan dari penelitian ini adalah untuk memprediksi jumlah mahasiswa yang kuliah di jurusan teknik informatika UPN Veteran Yogyakarta untuk periode yang akan datang. Data penelitian ini diambil dari forlap dikti untuk Teknik Informatika dari tahun 2009 sampai 2016 UPN Veteran Yogyakarta. Metode yang digunakan untuk melakukan peramalan jumlah mahasiswa adalah metode Moving Average yang tediri dari : Single Moving Average (SMA), Weighted Moving Average (WMA) dan Exponential Moving Average (EMA). Penelitian ini akan menggunkan akurasi peramalan Mean Square Error (MSE), Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE) untuk memilih model terbaik yang akan digunakan untuk peramalan. Model terbaik yang digunakan untuk peramalan yaitu Weighted Moving Average (WMA) dengan pembobot 1/3 dan panjang rata-rata (n) yang dipakai sebesar 2. Nilai terkecil untuk MSE sebesar 5807,96; nilai terkecil MAE sebesar 55,89 dan nilai terkecil untuk MAPE sebesar 5,24 %. Peramalan untuk jumlah mahasiswa empat semester kedepan setelah semester genap 2016 masing-masing adalah : 902; 901,33; 901,56 dan 901,48. Kata Kunci : Peramalan, UPN Veteran Yogyakarta, Single Moving Average(SMA).

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Yoo, Tae-Woong, and Il-Seok Oh. "Time Series Forecasting of Agricultural Products’ Sales Volumes Based on Seasonal Long Short-Term Memory." Applied Sciences 10, no. 22 (November 18, 2020): 8169. http://dx.doi.org/10.3390/app10228169.

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In this paper, we propose seasonal long short-term memory (SLSTM), which is a method for predicting the sales of agricultural products, to stabilize supply and demand. The SLSTM model is trained using the seasonality attributes of week, month, and quarter as additional inputs to historical time-series data. The seasonality attributes are entered into the SLSTM network model individually or in combination. The performance of the proposed SLSTM model was compared with those of auto_arima, Prophet, and a standard LSTM in terms of three performance metrics (mean absolute error (MAE), root mean squared error (RMSE), and normalization mean absolute error (NMAE)). The experimental results show that the error rate of the proposed SLSTM model is significantly lower than those of other classical methods.

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Guo, Hai, Jinghua Yin, Jingying Zhao, Lei Yao, Xu Xia, and Hao Luo. "An Ensemble Learning for Predicting Breakdown Field Strength of Polyimide Nanocomposite Films." Journal of Nanomaterials 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/950943.

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Using the method of Stochastic Gradient Boosting, ten SMO-SVR are constructed into a strong prediction model (SGBS model) that is efficient in predicting the breakdown field strength. Adopting the method of in situ polymerization, thirty-two samples of nanocomposite films with different percentage compositions, components, and thicknesses are prepared. Then, the breakdown field strength is tested by using voltage test equipment. From the test results, the correlation coefficient (CC), the mean absolute error (MAE), the root mean squared error (RMSE), the relative absolute error (RAE), and the root relative squared error (RRSE) are 0.9664, 14.2598, 19.684, 22.26%, and 25.01% with SGBS model. The result indicates that the predicted values fit well with the measured ones. Comparisons between models such as linear regression, BP, GRNN, SVR, and SMO-SVR have also been made under the same conditions. They show that CC of the SGBS model is higher than those of other models. Nevertheless, the MAE, RMSE, RAE, and RRSE of the SGBS model are lower than those of other models. This demonstrates that the SGBS model is better than other models in predicting the breakdown field strength of polyimide nanocomposite films.

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Khalis Sofi, Aswan Supriyadi Sunge, Sasmitoh Rahmad Riady, and Antika Zahrotul Kamalia. "PERBANDINGAN ALGORITMA LINEAR REGRESSION, LSTM, DAN GRU DALAM MEMPREDIKSI HARGA SAHAM DENGAN MODEL TIME SERIES." SEMINASTIKA 3, no. 1 (November 1, 2021): 39–46. http://dx.doi.org/10.47002/seminastika.v3i1.275.

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Penelitian ini bertujuan untuk memprediksi harga saham dengan membandingkan algoritma Linear Regression, Long Short-Term Memory (LSTM), dan Gated Recurrent Unit (GRU) dengan dataset publik kemudian menentukan performa terbaik dari ketiga algoritma tersebut. Dataset yang diuji bersumber dari Indonesia Stock Exchange (IDX), yaitu dataset harga saham KEJU berbentuk time series dari tanggal 15 November 2019 sampai dengan 08 Juni 2021. Parameter yang digunakan untuk pengukuran perbandingan adalah RMSE (Root Mean Square Error), MSE (Mean Square Error), dan MAE (Mean Absolute Error). Setelah dilakukan proses training dan testing, dihasilkan sebuah analisis bahwa dari hasil perbandingan algoritma yang digunakan, algoritma Gated Recurrent Unit (GRU) memiliki performance paling baik dibandingkan Linear Regression dan Long-Short Term Memory (LSTM) dalam hal memprediksi harga saham, dibuktikan dengan nilai RMSE, MSE, dan MAE dari uji coba GRU paling rendah, yaitu nilai RMSE 0.034, MSE 0.001, dan nilai MAE 0.024.

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Makatjane, Katleho Daniel, Edward Kagiso Molefe, and Roscoe Bertrum Van Wyk. "The Analysis of the 2008 US Financial Crisis: An Intervention Approach." Journal of Economics and Behavioral Studies 10, no. 1(J) (March 15, 2018): 59–68. http://dx.doi.org/10.22610/jebs.v10i1(j).2089.

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The current study investigates the impact of the 2008 US financial crises on the real exchange rate in South Africa. The data used in this empirical analysis is for the period from January 2000 to June 2017. The Seasonal autoregressive integrated moving average (SARIMA) intervention charter was used to carry out the analysis. Results revealed that the financial crises period in South Africa occurred in March 2008 and significantly affected the exchange rate. Hence, the impact pattern was abrupt. Using the SARIMA model as a benchmark, four error metrics; to be precise mean absolute error (MAE), mean absolute percentage error (MAPE), mean error (ME) and Mean percentage error (MPE) was used to assess the performance of the intervention model and SARIMA model. The results of the SARIMA intervention model produced better forecasts as compared to that one of SARIMA model.

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

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