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1
Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, no. 1 (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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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 (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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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 (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 (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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Ren, Tao, Xiaoqing Kang, Wen Sun, and Hong Song. "Study of Dynamometer Cards Identification Based on Root-Mean-Square Error Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 02 (2017): 1850004. http://dx.doi.org/10.1142/s0218001418500040.
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The surface dynamometer cards are important working condition data of sucker-rod pumping system. It has a very important practical significance for the analysis of transmission system and the diagnosis of oil production condition of sucker-rod pumping system. The pump dynamometer cards are important reference for the diagnosis of oil production condition, and its key technology is the identification of pump dynamometer cards. A new similar pattern recognition algorithm based on root-mean-square error (RMSE) is proposed, a theoretical model of the similarity matching algorithm based on RMSE is established, and the algorithm is studied and analyzed. The three-dimensional vibration mathematical models for the surface dynamometer cards are created, by which the surface dynamometer cards can be transformed to the pump dynamometer cards. The accuracy, reliability and stability between the algorithm of RMSE similarity matching and the classical algorithms of similarity pattern matching are studied. The research shows that the resistance to the graphics deformation of RMSE algorithm is the highest among all algorithms. The application of RMSE algorithm and classic similarity matching algorithms to the identification of real pump dynamometer cards and the fault diagnosis of oil wells indicates that the RMSE algorithm has very high identification reliability and accuracy. The remarkable feature of the RMSE algorithm is that it has very high identification accuracy for small difference, while the classical similarity matching algorithms do not have this feature.
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Karno, Adhitio Satyo Bayangkari. "Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory)." Journal of Informatic and Information Security 1, no. 1 (2020): 1–8. http://dx.doi.org/10.31599/jiforty.v1i1.133.
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Abstract
 
 This study aims to measure the accuracy in predicting time series data using the LSTM (Long Short-Term Memory) machine learning method, and determine the number of epochs needed to produce a small RMSE (Root Mean Square Error) value. The result of this research is a high level of variation in RMSE value to the number of epochs needed in the data processing. This variation is quite difficult to obtain the right epoch value. By doing an iteration of the LSTM process on the number of different epochs (visualized in the graph), then the number of epochs with a minimum RMSE value will be easier to obtain. From the research of BBRI's stock data prediction, a good RMSE value was obtained (RMSE = 227.470333244533).
 
 Keywords: long short-term memory, machine learning, epoch, root mean square error, mean square error.
 
 Abstrak
 
 Penelitian ini bertujuan untuk mengukur ketelitian dalam memprediksi data time series menggunakan metode mesin belajar LSTM (Long Short-Term Memory), serta menentukan banyaknya epoch yang diperlukan untuk menghasilkan nilai RMSE (Root Mean Square Error) yang kecil. Hasil dari penelitian ini adalah tingkat variasi yang tinggi nilai rmse terhdap jumlah epoch yang diperlukan dalam proses pengolahan data. Variasi ini cukup menyulitkan untuk memperoleh nilai epoch yang tepat. Dengan melakukan iterasi dari proses LSTM terhadap jumlah epoch yang berbeda (di visualisasikan dalam grafik), maka jumlah epoch dengan nilai RMSE minimal akan lebih mudah diperoleh. Dari penelitan prediksi data saham BBRI diperoleh nilai RMSE yang cukup baik yaitu 227,470333244533.
 Kata kunci: long short-term memory, machine learning, epoch, root mean square error, mean square error.
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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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Ivan, Eliansion, and Hindriyanto Dwi Purnomo. "FORECASTING PRICES OF FERTILIZER RAW MATERIALS USING LONG SHORT TERM MEMORY." Jurnal Teknik Informatika (Jutif) 3, no. 6 (2022): 1663–73. http://dx.doi.org/10.20884/1.jutif.2022.3.6.433.
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This study uses long short term memory (LSTM) modeling to predict time series data on the price of fertilizer raw materials, namely prilled urea, granular urea, ammonium sulphate((NH4)2SO4), ammonia (NH3), diammonium phosphate((NH4)2HPO4 ), phosphoric acid (H3PO4), phosphate rock (P2O5), NPK 16-16-16, potash, sulfur, and sulfuric acid (H2SO4). Predictions are made based on data that existed in the past using the long short term memory method, which is a derivative of the recurrent neural network. Carry out the evaluation process by looking at the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the model that has been created. The results obtained are quite good, as seen from the root mean square error (RMSE) and mean absolute percentage error (MAPE) which are close to 0 and not too high. Sulfur raw material got the smallest root mean square error (RMSE) with a score of 0.053 and diammonium phosphate raw material got the smallest mean absolute percentage error (MAPE) evaluation value with 2.3%, while the largest value was for the root mean square error (RMSE) of raw materials. Phosphoric acid fertilizer raw material with a value of 22,979 and the largest mean absolute percentage error (MAPE) comes from sulfuric acid fertilizer raw material with a value of 9.180%.
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Purva, Sharma, Saini Deepak, and Saxena Akash. "Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN." Bulletin of Electrical Engineering and Informatics 5, no. 3 (2016): 284–95. https://doi.org/10.11591/eei.v5i3.537.
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Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed that ANN architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network.
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Ganji, Homayoon, and Takamitsu Kajisa. "Error propagation approach for estimating root mean square error of the reference evapotranspiration when estimated with alternative data." Journal of Agricultural Engineering 50, no. 3 (2019): 120–26. http://dx.doi.org/10.4081/jae.2019.909.
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Estimation of reference evapotranspiration (ET0) with the Food and Agricultural Organisation (FAO) Penman-Monteith model requires temperature, relative humidity, solar radiation, and wind speed data. The lack of availability of the complete data set at some meteorological stations is a severe restriction for the application of this model. To overcome this problem, ET0 can be calculated using alternative data, which can be obtained via procedures proposed in FAO paper No.56. To confirm the validity of reference evapotranspiration calculated using alternative data (ET0(Alt)), the root mean square error (RMSE) needs to be estimated; lower values of RMSE indicate better validity. However, RMSE does not explain the mechanism of error formation in a model equation; explaining the mechanism of error formation is useful for future model improvement. Furthermore, for calculating RMSE, ET0 calculations based on both complete and alternative data are necessary. An error propagation approach was introduced in this study both for estimating RMSE and for explaining the mechanism of error formation by using data from a 30-year period from 48 different locations in Japan. From the results, RMSE was confirmed to be proportional to the value produced by the error propagation approach (ΔET0). Therefore, the error propagation approach is applicable to estimating the RMSE of ET0(Alt) in the range of 12%. Furthermore, the error of ET0(Alt) is not only related to the variables’ uncertainty but also to the combination of the variables in the equation.
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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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Usman, U., N. Garba, A.B Zoramawa, and H. Usman. "Assessing the Performance of Ordinary Least Square and Kernel Regression." Continental J. Applied Sciences 15, no. 1 (2020): 14–23. https://doi.org/10.5281/zenodo.3764305.
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The assessment of Ordinary Least Squares (OLS) and kernel regression on their predictive performance was studied. We used simulated data to assess the performance of estimators using small and large sample. However, the mean square error (MSE) and root mean square error (RMSE) was used to find out the most efficient among the estimated models. The results show that, when the ordinary least square is more efficient than the kernel regression due to having the least MSE and RMSE in both distributions. Whereas for the ordinary least square and the kernel regression have the same performance for normal distributed data while for lognormal, the result also shows that the kernel regression perform better than the ordinary least square. Finally, when, the kernel regression is more efficient than the ordinary least square for having the least MSE and RMSE in both distributions. The overall results show that the kernel regression estimate is more efficient than the ordinary least square (OLS) estimate.
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Thomas, Oka Pratama, Sunarno Sunarno, Budhie Wijatna Agus, and Haryono Eko. "Grindulu fault cloud radon data for earthquake magnitude prediction using machine learning." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4572–82. https://doi.org/10.11591/ijai.v13.i4.pp4572-4582.
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The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grindulu fault, the research employs random forest (RF), extreme gradient boosting (XGB), neural network (NN), AdaBoost (AB), and support vector machine (SVM) methods. The study aims to refine earthquake magnitude prediction, utilizing real-time radon gas concentration measurements, crucial for disaster preparedness. The evaluation involves multiple metrics like mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), mean squared error (MSE), symmetric mean absolute percentage error (SMAPE), and conformal normalized mean absolute percentage error (cnSMAPE). XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnSMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.
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Hendarwati, Emy Khairil, Piter Lepong, and Suyitno Suyitno. "Pemilihan Semivariogram Terbaik Berdasarkan Root Mean Square Error (RMSE) pada Data Spasial Eksplorasi Emas Awak Mas." GEOSAINS KUTAI BASIN 6, no. 1 (2023): 47. http://dx.doi.org/10.30872/geofisunmul.v6i1.1072.
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Semivariogram merupakan perangkat dasar geostatistik yang digunakan untuk memvisualisasi, memodelkan, dan menghitung autokorelasi spasial dari antar data dalam suatu variabel. Semivariogram dibedakan menjadi dua, yaitu semivariogram eksperimental dan semivariogram teoritis. Terdapat tiga jenis model semivariogram teoritis, yaitu model spherical, model eksponensial, dan model gaussian. Penelitian ini bertujuan untuk menentukan model semivariogram terbaik berdasarkan nilai RMSE terkecil. Data penelitian ini adalah data sekunder eksplorasi emas yang terdiri dari data drillhole sebanyak 101 data. Proses pemilihan model semivariogram dimulai dengan menghitung semivariogram eksperimental, melakukan analisis struktural dengan mencocokkan kurva semivariogram eksperimental dengan kurva semivariogram teoritis model spherical, model eksponensial, dan model gaussian, diperoleh nilai nugget, sill, dan range pada masing – masing model semivariogram, menghitung nilai RMSE pada model spherical, model eksponensial, dan model gaussian. Nilai RMSE pada model spherical sebesar 0,3259, model eksponensial sebesar 0,2655, dan model gaussian sebesar 0,3224. Berdasarkan hasil RMSE, model semivariogram terbaik dengan nilai RMSE terkecil adalah model eksponensial.
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Aviantoro, Kevin, and Yulia Darnita. "IMPLEMENTASI WIENER, CONTRAST STRETCHING, SHARPENING FILTER PADA CITRA SEMANGKA MENGGUNAKAN MSE,RMSE, DAN PSNR." Djtechno: Jurnal Teknologi Informasi 5, no. 2 (2024): 195–205. http://dx.doi.org/10.46576/djtechno.v5i2.4613.
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Penelitian ini mengkaji tiga metode pemrosesan citra Wiener Filter, Contrast Stretching, dan Sharpening Filter untuk meningkatkan kualitas citra semangka. Evaluasi kinerja dilakukan menggunakan Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), dan Root Mean Square Error (RMSE). Wiener Filter efektif mengurangi noise, Contrast Stretching meningkatkan kontras, dan Sharpening Filter menonjolkan detail. MSE mengukur rata-rata kesalahan kuadrat antara citra asli dan citra yang diproses, dengan nilai < 1 menunjukkan kualitas bagus dan > 1 kualitas kurang bagus. PSNR mengukur rasio sinyal maksimum terhadap noise, dengan nilai < 20 dB menunjukkan kualitas kurang bagus dan > 40 dB kualitas bagus. RMSE, sebagai akar dari MSE, memberikan ukuran kesalahan absolut antara citra asli dan citra yang diproses. Nilai RMSE yang lebih rendah menunjukkan kualitas yang lebih baik dan memudahkan pemahaman besaran kesalahan.
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Andiani, Andiani, Yoel Simanjuntak, and Ninuk Wiliani. "Performance Assessment of ARIMA and LSTM Models in Prediction Using Root Mean Square Error (RMSE)." Journal of Applied Research In Computer Science and Information Systems 2, no. 1 (2024): 149–58. https://doi.org/10.61098/jarcis.v2i1.181.
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Cryptocurrency is a digital financial asset that serves as a medium of exchange, with its ownership guaranteed using decentralized cryptographic technology, and it has become a growing investment tool. Solana is one of the highly sought-after Cryptocurrencies by investors. The market price of Solana exhibits highly volatile movements, which are considered risky for investment purposes, as it offers both high potential profits and losses. In this regard, time series data prediction models are used to analyze and forecast the price movements of Solana. By comparing the performance of ARIMA and LSTM models in predicting the closing price of Solana using RMSE as a testing metric, the aim is to determine the efficiency level of both ARIMA and LSTM models. The research results show that the ARIMA model with an order of (2,1,3) achieves an RMSE of 0.019 (1.9%) with an accuracy of 98.1%, while the LSTM model with a data training ratio of 70:30%, a batch size of 64, and 500 epochs has an RMSE of 0.075 (7.5%) with an accuracy of 92.5%. The conclusion drawn from the conducted experiments is that, in the case of using time series data samples from Solana, the ARIMA method demonstrates higher accuracy compared to the LSTM method.
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Fortin, V., M. Abaza, F. Anctil, and R. Turcotte. "Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?" Journal of Hydrometeorology 15, no. 4 (2014): 1708–13. http://dx.doi.org/10.1175/jhm-d-14-0008.1.
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Abstract When evaluating the reliability of an ensemble prediction system, it is common to compare the root-mean-square error of the ensemble mean to the average ensemble spread. While this is indeed good practice, two different and inconsistent methodologies have been used over the last few years in the meteorology and hydrology literature to compute the average ensemble spread. In some cases, the square root of average ensemble variance is used, and in other cases, the average of ensemble standard deviation is computed instead. The second option is incorrect. To avoid the perpetuation of practices that are not supported by probability theory, the correct equation for computing the average ensemble spread is obtained and the impact of using the wrong equation is illustrated.
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Pratama, Thomas Oka, Sunarno Sunarno, Agus Budhie Wijatna, and Eko Haryono. "Cloud radon data for earthquake magnitude prediction using machine learning." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4572. http://dx.doi.org/10.11591/ijai.v13.i4.pp4572-4582.
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<span>The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grundulu fault, the research employs Random Forest (RF), Extreme Gradient Boosting (XGB), Neural Network (NN), AdaBoost (AB), and Support Vector Machine (SVM) methods. Utilizing real-time radon gas concentration measurements, the study aims to refine earthquake magnitude prediction, crucial for disaster preparedness. The evaluation involves multiple metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), Symmetric Mean Absolute Percentage Error (SMAPE), and cnSMAPE. XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.</span>
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Ahmad, Ayaz, Furqan Farooq, Pawel Niewiadomski, et al. "Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm." Materials 14, no. 4 (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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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 (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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Saglam, Mustafa, Catalina Spataru, and Omer Ali Karaman. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms." Energies 16, no. 11 (2023): 4499. http://dx.doi.org/10.3390/en16114499.
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Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey’s mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10−14, 28.35 × 10−28, and 2.5 × 10−14, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested.
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Fahri, Amin, and Yudi Ramdhani. "Visualisasi Data dan Penerapan Machine Learning Menggunakan Decision Tree Untuk Keputusan Layanan Kesehatan COVID-19." Jurnal Tekno Kompak 17, no. 2 (2023): 50. http://dx.doi.org/10.33365/jtk.v17i2.2438.
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Pada Desember 2019, virus corona baru yang sekarang dinamai SARS-CoV-2, menyebabkan serangkaian penyakit pernapasan atipikal akut di Wuhan, Provinsi Hubei, China. Penyakit yang disebabkan oleh virus ini disebut COVID-19. Virus ini dapat menular antar manusia dan telah menyebabkan pandemi di seluruh dunia. Virus yang mendasari penyakit COVID-19, SARS-CoV-2, telah menyebabkan lebih dari 120 juta kasus yang dikonfirmasi dan 1,5 juta kematian sejak April 2022. Penelitian ini menggunakan algoritma Decision Tree untuk memprediksi COVID-19 dengan validasi parameter Cross Validation, Split Validation. Cross Validation pada algoritma Decision Tree Regressor memiliki tingkat performa terbaik diantara 3 algoritma lainnya, seperti; Linear Regression, Support Vector Machine Regression dan Neural Network Regression. Algoritma Decision Tree menghasilkan nilai average 57 untuk RMSE (Root Mean Square Error). Validasi data menggunakan split validation menghasilkan nilai average 29 untuk MAE (Mean Absolute Error) , 3816 untuk MSE (Mean Square Error), 59 untuk RMSE (Root Mean Square Error) dan 0,956 untuk R2 Square. Split ratio 0,9 memiliki nilai MAE, MSE, RMSE dan R2 Square tertinggi. Artinya algoritma Decision Tree Regressor memiliki kinerja yang baik untuk meningkatkan kinerja algoritma prediksi. Berdasarkan hasil penelitian mendapatkan nilai RMSE terbaik sehingga bisa digunakan oleh tenaga medis dan peneliti dalam melakukan prediksi COVID-19 dan dapat menjadi bahan rujukan metode yang akan diimplementasikan pada saat membuat penelitian mengenai COVID-19.
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Al Mahrouq, Yousef A. "Effect of Item Difficulty and Sample Size on the Accuracy of Equating by Using Item Response Theory." Journal of Educational and Psychological Studies [JEPS] 10, no. 1 (2016): 182–200. http://dx.doi.org/10.53543/jeps.vol10iss1pp182-200.
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This study explored the effect of item difficulty and sample size on the accuracy of equating by using item response theory. This study used simulation data. The equating method was evaluated using an equating criterion (SEE, RMSE). Standard error of equating between the criterion scores and equated scores, and root mean square error of equating (RMSE) were used as measures to compare the method to the criterion equating. The results indicated that the large sample size reduces the standard error of the equating and reduces residuals. The results also showed that different difficulty models tend to produce smaller standard errors and the values of RMSE. The similar difficulty models tend to produce decreasing standard errors and the values of RMSE.
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Al Mahrouq, Yousef A. "Effect of Item Difficulty and Sample Size on the Accuracy of Equating by Using Item Response Theory." Journal of Educational and Psychological Studies [JEPS] 10, no. 1 (2016): 182. http://dx.doi.org/10.24200/jeps.vol10iss1pp182-200.
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This study explored the effect of item difficulty and sample size on the accuracy of equating by using item response theory. This study used simulation data. The equating method was evaluated using an equating criterion (SEE, RMSE). Standard error of equating between the criterion scores and equated scores, and root mean square error of equating (RMSE) were used as measures to compare the method to the criterion equating. The results indicated that the large sample size reduces the standard error of the equating and reduces residuals. The results also showed that different difficulty models tend to produce smaller standard errors and the values of RMSE. The similar difficulty models tend to produce decreasing standard errors and the values of RMSE.
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Durmus, Fatih, and Serap Karagol. "Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP." Applied Sciences 14, no. 13 (2024): 5662. http://dx.doi.org/10.3390/app14135662.
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Over the last 20 years, lithium-ion batteries have become widely used in many fields due to their advantages such as ease of use and low cost. However, there are concerns about the lifetime and reliability of these batteries. These concerns can be addressed by obtaining accurate capacity and health information. This paper proposes a method to predict the capacity of lithium-ion batteries with high accuracy. Four key features were extracted from current and voltage data obtained during charge and discharge cycles. To enhance prediction accuracy, the Pearson correlation coefficient between these features and battery capacities was analyzed and eliminations were made for some batteries. Using a genetic algorithm (GA), the parameter optimization of Convolutional Neural Network (CNN), Backpropagation (BP), and Recurrent Neural Network (RNN) algorithms was performed. The parameters that provide the best performance were determined in a shorter time using GA, which includes natural selection and genetic processes instead of a trial-and-error method. The study employed five metrics—Mean Square Error (MSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), and Squared Correlation (R2)—to evaluate prediction accuracy. Predictions based on NASA experimental data were compared with the existing literature, demonstrating superior accuracy. Using 100 training data, 68 data predictions were made with a Root Mean Square Error (RMSE) of 0.1176%. This error rate represents an accuracy level 2.5 times higher than similarly accurate studies in the literature.
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Hussain, Lal, Sharjil Saeed, Adnan Idris, et al. "Regression analysis for detecting epileptic seizure with different feature extracting strategies." Biomedical Engineering / Biomedizinische Technik 64, no. 6 (2019): 619–42. http://dx.doi.org/10.1515/bmt-2018-0012.
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Abstract Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.
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Nurfarawahida Ramly, Mohd Saifullah Rusiman, Efendi Nasibov, Resmiye Nasiboglu, and Suparman. "The Comparison of Fuzzy Regression Approaches with and without Clustering Method in Predicting Manufacturing Income." Journal of Advanced Research in Applied Sciences and Engineering Technology 46, no. 1 (2024): 218–36. http://dx.doi.org/10.37934/araset.46.1.218236.
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In the manufacturing area, predicting future income is more important to keep maintain their industry profits. In addition to this, most of the manufacturing company having a bit problem in predicting their manufacturing income, especially in terms of data and method used. Hence, this paper proposed another improvise method of fuzzy regression approach with and without clustering method for uses of predicting manufacturing income. Then, one of the widely uses of statistical analysis are fuzzy regression approach such as fuzzy linear regression (FLR) and fuzzy least squares regression (FLSR). Furthermore, clustering is one of the most common methods for grouping data based on its similarity. Apart from this, fuzzy c-means (FCM) recognised as the best clustering method. This study's model was evaluated by three measurements errors: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Based on numerical calculations, it was determined that the proposed fuzzy least square regression with fuzzy c-means clustering model is superior to others, with RMSE = 59756.78229, MAE = 2948.616554, and MAPE = 13.34916083. Therefore, this model indicates as the robust method and suitable use for prediction analysis, especially in handling uncertain and imprecise data.
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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 (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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Kamaruddin, Nor Kamariah, Muhammad Ammar Shafi, Gusman Nawanir, Nur Azia Hazida Mohamad Azmi, Aliya Syaffa Zakaria, and Zulfana Lidinillah. "Performance of Linear Programming Asymmetric Parameter Fuzzy Modelling Based on Statistical Error Measurement." Journal of Advanced Research Design 130, no. 1 (2025): 126–33. https://doi.org/10.37934/ard.130.1.126133.
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Modelling the relationship between a scalar answer and one or more explanatory factors using a linear technique is known as linear regression. The problem of using linear regression arises with the use of uncertain and imprecise data. Since the fuzzy set theory’s concept can deal with data not to a precise point value (uncertainty data), this study applied the fuzzy linear regression with asymmetric parameter (FLRWAP) to 1000 row of simulation data. Five independent variables with different combination of variable types were considered. Other than that, the performance of the models such as the parameter, error and explanation for the model were included using two measurement statistical errors which is mean square error and root mean square error. FLRWAP found the results of least value of mean square error (MSE) and root mean square error (RMSE) is less than another model with 107.88 and 10.39 respectively.
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Gede Adi, Wiguna Sudiartha, Oginawati Katharina, Sofyan Asep, et al. "One-Dimensional Pollutant Transport Modelling of Cadmium (Cd), Chromium (Cr) and Lead (Pb) in Saguling Reservoir." E3S Web of Conferences 148 (2020): 07009. http://dx.doi.org/10.1051/e3sconf/202014807009.
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The existing conditions of the Saguling Reservoir are reported to have suffered severe heavy metal pollution due to the presence of wastewater inputs from various types of industries flowing into Citarum River and then accumulating in the Saguling Reservoir. From the results of calibration tests of heavy metal models on water using the Root Mean Square Error (RMSE) analysis and Relative Error (RE) analysis, obtained dispersion coefficients on Cadmium, Chromium, and Lead metals sequentially 1 m2 / second (with RMSE 0,00515 and 34% relative error); 1 m2 / second (with RMSE 0.00595 and relative error 26%); and 2.5 m2 / second (with RMSE 0.028205 and relative error 41.25%) which shows that the model has good capability to simulate the concentration of heavy metals approaching the actual data both in the dry and wet seasons. From the results of the verification test models of concentration of cadmium, lead and chromium in sediments using the Root Mean Square Error (RMSE) analysis and Relative Error (RE) analysis, obtained sequentially 18.53 and 77%; 10.43 and 47.15%; 2.789 and 33%. Error values in sediment concentrations are quite large because of the difficulty of making assumptions that are close to natural conditions.
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Xu, H., R. De Jong, S. Gameda, and B. Qian. "Development and evaluation of a Canadian agricultural ecodistrict climate database." Canadian Journal of Soil Science 90, no. 2 (2010): 373–85. http://dx.doi.org/10.4141/cjss09064.
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Spatially representative climate data are required input in various agricultural and environmental modelling studies. An agricultural ecodistrict climate database for Canada was developed from climate station data using a spatial interpolation procedure. This database includes daily maximum and minimum air temperatures, precipitation and incoming global solar radiation, which are necessary inputs for many agricultural modelling studies. The spatial interpolation procedure combines inverse distance squared weighting with the nearest neighbour approach. Cross-validation was performed to evaluate the accuracy of the interpolation procedure. In addition to some common error measurements, such as mean biased error and root mean square error, empirical probability distributions and accurate rates of precipitation occurrence were also examined. Results show that the magnitude of errors for this database was similar to those in other studies that used similar or different interpolation procedures. The average root mean square error (RMSE) was 1.7°C, 2.2°C and 3.8 mm for daily maximum and minimum temperature, and precipitation, respectively. The RMSE for solar radiation varied from 16 to 19% of the climate normal during April through September and from 21 to 28% of the climate normal during the remainder of the year.Key words: Maximum and minimum temperature, precipitation, solar radiation, ecodistrict, interpolation, cross validation
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Prasetiamaolana, Eko, and Mohammad Syafrullah. "The Use of Single Moving Average and Linear Regression in Spare Part Sales Forecasting at PT. CNC." INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY ENGINEERING AND INFORMATION SYSTEM (IJATEIS) 4, no. 1 (2025): 64–72. https://doi.org/10.55047/ijateis.v4i1.1587.
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Given the highly competitive nature of Indonesia's automotive sector, accurate sales forecasting has become a crucial business strategy. This research investigates the application of Single Moving Average and Linear Regression methods for forecasting spare part sales at PT. CNC, an automotive spare parts manufacturer in Indonesia. The study analyzes monthly sales data from January 2019 to December 2022, employing both Single Moving Average and Linear Regression forecasting methods. Model performance was evaluated using multiple accuracy metrics including Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with data normalized using min-max normalization. The analysis yielded error metrics of MSE = 0.043, RMSE = 0.208, MAE = 0.005, and MAPE = 4.36%, demonstrating the effectiveness of these forecasting methods for spare part sales prediction.
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Herranz-Matey, Ivan, and Luis Ruiz-Garcia. "New Agricultural Tractor Manufacturer’s Suggested Retail Price (MSRP) Model in Europe." Agriculture 14, no. 3 (2024): 342. http://dx.doi.org/10.3390/agriculture14030342.
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Research investigating models for assessing new tractor pricing is notably scarce, despite its fundamental importance in conducting comprehensive cost analyses. This study aims to identify a model that is both user-friendly and robust, evaluating both parametric and Machine Learning-optimized non-parametric models. Among parametric models, the second-order polynomial model demonstrated superior performance in terms of R-squared (R2) of 0.97469 and a Root Mean Square Error (RMSE) of 15,633. Conversely, Machine Learning-optimized Gaussian Processes Regressions exhibited the most favorable overall R-squared (R2) of 0.99951 and a Root Mean Square Error (RMSE) of 2321. While the parametric polynomial model offers a solution with minimal mathematical and computational complexity, the non-parametric GPR model delivers highly robust outcomes, presenting stakeholders involved in new agriculture tractor transactions with superior data-driven decision-making capabilities.
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Ikenna, Ukabuiro, and Stella Agomah. "Prediction Models for Forex Data Exchange System." Prediction Models for Forex Data Exchange System 8, no. 12 (2024): 4. https://doi.org/10.5281/zenodo.10453255.
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Foreign exchange prediction is of important interest to investors and individual traders in financial industries in other to maximize profits and reduces losses. However owing to some factors and the non- linearity of the FX markets especially in a developing economy like Nigeria, generating suitable, accurate and appropriate FX predictions becomes difficult for the traders of the market. This study utilized models that include various machine learning algorithm over a trend analysis and pattern of its prediction. The model results on the currency pair of United States(USD) over Nigeria Naira (NGN) using Root Mean Squared Error (RMSE), Mean Absolute Error(MAE), Mean Square Error (MSE), and R-square (R2) showed GRU performed better in predicting the trend and we therefore considered it best fit for the forecast. The result showed high prediction over ANN and LSTM, with RMSE, MAE, MSE, and R2 values of 0.112, 0.075, 0.013, and 0.969. Keywords:- Forex, ANN, LSTM, GRU MAE, MSE.
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Mufarroha, Fifin Ayu, Akhmad Tajuddin Tholaby, Devie Rosa Anamisa, and Achmad Jauhari. "Prediction Model for Tourism Object Ticket Determination in Bangkalan, Madura, Indonesia." ComTech: Computer, Mathematics and Engineering Applications 14, no. 2 (2023): 69–81. http://dx.doi.org/10.21512/comtech.v14i2.7992.
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One of the regencies in Madura, namely Bangkalan, with its local wisdom and beautiful landscapes has the potential to become a tourism center. However, there may be a decrease in the number of visits caused by some factors. The research used the time series method to build a prediction model for tourist attraction entrance tickets. The model development aimed to estimate the number of tourist attraction visits in the future. The right model was needed to get the best prediction results. Least square, Holt-Winter, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Rolling were chosen as the models. Data collection related to the number of tourist objects was carried out directly at the Tourism Office to obtain valid data. Using data on visitors to tourist attractions in Bangkalan Regency from 2015 to 2019, the results of measuring errors using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) are obtained. The error measurement results show that the Holt-Winter model has the lowest error rate of 5% and RMSE of 307,1198. Based on these calculations, the Holt-Winter model is the best model for determining tourist attraction entrance tickets. The ranking of the error measurement results from the highest to the lowest are Holt-Winter, Rolling, SARIMA, and Least Square methods.
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Zamzami, Balqis Dwian Fitri, Aisyah Tiara Pratiwi, Della Septiani, et al. "Algoritma Alternating Least Squares Untuk Mesin Rekomendasi Film." PROSIDING SEMINAR NASIONAL SAINS DATA 4, no. 1 (2024): 341–50. https://doi.org/10.33005/senada.v4i1.210.
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Abstract: The entertainment world is inseparable from the rapidly growing movie industry and is accompanied by huge data growth. The rapid growth of data has brought about a new era of information. These data are utilized to build innovative, efficient and more effective systems. This research implements a movie recommendation system using the Alternating Least Squares (ALS) algorithm from Apache Spark MLlib with the MovieLens 25M dataset. A collaborative filtering approach with matrix factorization is used to model user preferences and movie characteristics. The evaluation is done by calculating the Root Mean Squared Error (RMSE) on the test data. The first ALS model with default parameters produced the best RMSE of 0.811671, while the second model with different parameters produced an RMSE of 0.839577. The results show that the ALS model with default parameters is able to provide accurate and relevant movie recommendations according to user preferences Keywords: Film, Recommendation, Big Data, Alternating Least Square, Pyspark Abstrak: Dunia hiburan tak terlepas dari industri film yang berkembang sangat pesat dan disertai dengan pertumbuhan data yang sangat besar. Pesatnya pertumbuhan data telah membawa era baru informasi. Data-data ini dimanfaatkan untuk membangun sistem-sistem yang inovatif, efisien dan lebih efektif. Penelitian ini mengimplementasikan sistem rekomendasi film menggunakan algoritma Alternating Least Squares (ALS) dari Apache Spark MLlib dengan dataset MovieLens 25M. Pendekatan collaborative filtering dengan matrix factorization digunakan untuk memodelkan preferensi pengguna dan karakteristik film. Evaluasi dilakukan dengan menghitung Root Mean Squared Error (RMSE) pada data uji. Model ALS pertama dengan parameter default menghasilkan RMSE terbaik sebesar 0.811671, sementara model kedua dengan parameter yang berbeda menghasilkan RMSE 0.839577. Hasil menunjukkan bahwa model ALS dengan parameter default mampu memberikan rekomendasi film yang akurat dan relevan sesuai preferensi pengguna Kata kunci: Alternating Least Square, Big Data, Film, Pyspark, Rekomendasi
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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 (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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Shafi, Muhammad Ammar, Mohd Saifullah Rusiman, Kavikumar Jacob, Nor Shamsidah Amir Hamzah, Norziha Che Him, and Nazeera Mohamad. "Prediction in a Hybrid of Fuzzy Linear Regression with Symmetric Parameter Model and Fuzzy C-Means Method Using Simulation Data." International Journal of Engineering & Technology 7, no. 4.30 (2018): 419. http://dx.doi.org/10.14419/ijet.v7i4.30.22351.
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The objective of fuzzy linear regression model (FLRM) to predict the dependent variable and independent variables in vague phenomenon. In this study, several models such as fuzzy linear regression model (FLRM), fuzzy linear regression with symmetric parameter (FLWSP) and a hybrid model have been applied to be evaluated by 1000 rows in 1 simulation data. Moreover, the hybrid method was applied between fuzzy linear regression with symmetric parameter (FLRWSP) and fuzzy c-mean (FCM) method to get the effective prediction in a new model and best result in this study. To improve the accuracy of evaluating and predicting, this study employ two measurement error of cross validation statistical technique which are mean square error (MSE) and root mean square error (RMSE). The simulation result suggests that comparison among models using two measurement errors should be to determine the best results. Finally, this study notes that the new hybrid model of FLRWSP and FCM is verified to be a good model with the least value of MSE and RMSE measurement errors.
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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 (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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Almaliki, Abdulrazak H. "Estimating Tidal Sea levels along the Central Coast of the Western Arabian Gulf using Machine Learning Algorithms (MLAs)." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 2 (2024): 1–6. http://dx.doi.org/10.35940/ijrte.b8073.13020724.
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Precise tidal forecasting is an academic exercise and a crucial tool for designing and constructing coastal and marine infrastructure. Machine learning algorithms (MLAs) like Random Forest Regression (RF), K-Nearest Neighbors Regression (KN), Gradient Boosting Machines (GBM), and artificial neural networks (ANNs) are powerful data-driven techniques that can be harnessed for this practical purpose. This study utilizes four machine learning algorithms (MLAs), namely (RF), (KN), (GBM), and the Artificial Neural Network - Multilayer Perceptron (ANN-MLP) model, to accurately estimate the tidal levels along the central coast of the western Arabian Gulf, with direct implications for real-world infrastructure planning and construction. Several metrics, such as mean absolute error (MAE), mean squared error (MSE), normalized mean square error (NMSE), mean absolute percentage error (MAPE), correlation coefficient (R), and root mean square error (RMSE), are used to compare how well the MLAs forecast daily tidal levels. The results confirmed the ANN-MLP model's superiority over the other approaches. The ANN-MLP model, a specific type of artificial neural network, yields enhancements in (RMSE) of 8.945% and 19.05%, 14.18% compared to (RF), (KN), and (GBM), respectively, throughout the testing process. The ANN-MLP, being a powerful and versatile machine learning algorithm, demonstrated the best level of accuracy, together with the lowest values for (RMSE). This experiment unequivocally proves that the ANN-MLP method can be utilized as a supervised machine-learning method for accurately forecasting seawater levels of tidal.
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Almaliki, Abdulrazak H. "Estimating Tidal Sea levels along the Central Coast of the Western Arabian Gulf using Machine Learning Algorithms (MLAs)." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 2 (2024): 1–6. http://dx.doi.org/10.35940/ijrte.b8073.1302072.
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Precise tidal forecasting is an academic exercise and a crucial tool for designing and constructing coastal and marine infrastructure. Machine learning algorithms (MLAs) like Random Forest Regression (RF), K-Nearest Neighbors Regression (KN), Gradient Boosting Machines (GBM), and artificial neural networks (ANNs) are powerful data-driven techniques that can be harnessed for this practical purpose. This study utilizes four machine learning algorithms (MLAs), namely (RF), (KN), (GBM), and the Artificial Neural Network - Multilayer Perceptron (ANN-MLP) model, to accurately estimate the tidal levels along the central coast of the western Arabian Gulf, with direct implications for real-world infrastructure planning and construction. Several metrics, such as mean absolute error (MAE), mean squared error (MSE), normalized mean square error (NMSE), mean absolute percentage error (MAPE), correlation coefficient (R), and root mean square error (RMSE), are used to compare how well the MLAs forecast daily tidal levels. The results confirmed the ANN-MLP model's superiority over the other approaches. The ANN-MLP model, a specific type of artificial neural network, yields enhancements in (RMSE) of 8.945% and 19.05%, 14.18% compared to (RF), (KN), and (GBM), respectively, throughout the testing process. The ANN-MLP, being a powerful and versatile machine learning algorithm, demonstrated the best level of accuracy, together with the lowest values for (RMSE). This experiment unequivocally proves that the ANN-MLP method can be utilized as a supervised machine-learning method for accurately forecasting seawater levels of tidal.
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Abdulrazak, H. Almaliki. "Estimating Tidal Sea levels along the Central Coast of the Western Arabian Gulf using Machine Learning Algorithms (MLAs)." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 2 (2024): 1–6. https://doi.org/10.35940/ijrte.B8073.13020724.
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<strong>Abstract:</strong> Precise tidal forecasting is an academic exercise and a crucial tool for designing and constructing coastal and marine infrastructure. Machine learning algorithms (MLAs) like Random Forest Regression (RF), K-Nearest Neighbors Regression (KN), Gradient Boosting Machines (GBM), and artificial neural networks (ANNs) are powerful data-driven techniques that can be harnessed for this practical purpose. This study utilizes four machine learning algorithms (MLAs), namely (RF), (KN), (GBM), and the Artificial Neural Network - Multilayer Perceptron (ANN-MLP) model, to accurately estimate the tidal levels along the central coast of the western Arabian Gulf, with direct implications for real-world infrastructure planning and construction. Several metrics, such as mean absolute error (MAE), mean squared error (MSE), normalized mean square error (NMSE), mean absolute percentage error (MAPE), correlation coefficient (R), and root mean square error (RMSE), are used to compare how well the MLAs forecast daily tidal levels. The results confirmed the ANN-MLP model's superiority over the other approaches. The ANN-MLP model, a specific type of artificial neural network, yields enhancements in (RMSE) of 8.945% and 19.05%, 14.18% compared to (RF), (KN), and (GBM), respectively, throughout the testing process. The ANN-MLP, being a powerful and versatile machine learning algorithm, demonstrated the best level of accuracy, together with the lowest values for (RMSE). This experiment unequivocally proves that the ANN-MLP method can be utilized as a supervised machine-learning method for accurately forecasting seawater levels of tidal.
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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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Irhuma, Mohamed, Ahmad Alzubi, Tolga Öz, and Kolawole Iyiola. "Migrative armadillo optimization enabled a one-dimensional quantum convolutional neural network for supply chain demand forecasting." PLOS ONE 20, no. 3 (2025): e0318851. https://doi.org/10.1371/journal.pone.0318851.
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Demand forecasting is a quite challenging task, which is sensitive to several factors such as endogenous and exogenous parameters. In the context of supply chain management, demand forecasting aids to optimize the resources effectively. In recent years, numerous methods were developed for Supply Chain (SC) demand forecasting, which posed several limitations related to inadequate handling of dynamic time series patterns and data requirement problems. Thus, this research proposes a Migrative Armadillo Optimization-enabled one-dimensional Quantum convolutional neural network (MiA + 1D-QNN) for effective demand forecasting. The Migrative Armadillo Optimization (MAO) algorithm effectively optimizes the hyperparameters of the model. Specifically, the 1D-QNN approach offers exponential speed in the forecasting tasks as well as provides accurate prediction. Furthermore, the K-nearest Neighbor imputation technique fills the missing values, which preserves the data integrity as well as reliability. The experimental outcomes attained using the proposed model achieved a correlation of 0.929, Mean Square Error (MSE) of 7.34, Mean Absolute Error of 1.76, and Root Mean Square Error (RMSE) of 2.71 for the supply chain analysis dataset. For DataCo smart SC for big data analysis dataset, the MiA + 1D-QNN model achieved the correlation of 0.957, Mean Square Error (MSE) of 6.00, Mean Absolute Error of 1.62, and Root Mean Square Error (RMSE) of 2.45.
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El Marghichi, Mouncef, Soufiane Dangoury, Younes zahrou, et al. "Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications." PLOS ONE 18, no. 11 (2023): e0293753. http://dx.doi.org/10.1371/journal.pone.0293753.
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Significant improvements in battery performance, cost reduction, and energy density have been made since the advancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). The safety and effectiveness of EVs depend on accurate measurement and prediction of the state of health (SOH) of lithium-ion batteries; however, this process is uncertain. In this study, our primary goal is to enhance the accuracy of SOH estimation by reducing uncertainties in state of charge (SOC) estimation and measurements. To achieve this, we propose a novel method that utilizes the gradient-based optimizer (GBO) to evaluate the SOH of lithium batteries. The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. We evaluated our method against four robust algorithms, namely particle swarm optimization-least square support vector regression (PSO-LSSV), BCRLS-multiple weighted dual extended Kalman filtering (BCRLS-MWDEKF), Total least square (TLS), and approximate weighted total least squares (AWTLS) in hybrid electric vehicle (HEV) and electric vehicle (EV) applications. Our method consistently outperformed the alternatives, with the GBO achieving the lowest maximum error. In EV scenarios, GBO exhibited maximum errors ranging from 0.65% to 1.57% and mean errors ranging from 0.21% to 0.57%. Similarly, in HEV scenarios, GBO demonstrated maximum errors ranging from 0.81% to 3.21% and mean errors ranging from 0.39% to 1.03%. Furthermore, our method showcased superior predictive performance, with low values for mean squared error (MSE) (<1.8130e-04), root mean squared error (RMSE) (<1.35%), and mean absolute percentage error (MAPE) (<1.4).
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Nazrul, Effendy, David Kurniawan Eko, Dwiantoro Kenny, Arif Agus, and Muddin Nidlom. "The prediction of the oxygen content of the flue gas in a gas-fired boiler system using neural networks and rand." International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 923–29. https://doi.org/10.11591/ijai.v11.i3.pp923-929.
Full textAbstract:
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.
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Ferdiansyah, Hajar Othman Siti, Zahilah Md Radzi Raja, Stiawan Deris, and Sutikno Tole. "Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy." International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 251–61. https://doi.org/10.11591/ijai.v12.i1.pp251-261.
Full textAbstract:
Cryptocurrency is a digital currency used in financial systems that utilizes blockchain technology and cryptographic functions to gain transparency and decentralization. Because cryptocurrency prices fluctuate so much, tools for monitoring and forecasting them are required. Long short-term memory (LSTM) is a deep learning model that is capable of strongly predicting data time series. LSTM has been used in previous studies to predict the common currency. In this study, we used the gate recurrent unit (GRU) and bidirectional–LSTM (Bi-LSTM) hybrid model to predict cryptocurrency prices to improve the accuracy and normalize the root mean square error (RMSE) score of previously proposed prediction Using four cryptocurrencies (Bitcoin, Ethereum, Ripple, and Binance), the LSTM model predicts the Bitcoin. The RMSE obtained based on the best experimental results was 2343, Ethereum 10 epoch 203.89, Binance 200 epoch 32.61, and Ripple 200 epoch 0.077, while the mean absolute percentage error (MAPE) obtained for Bitcoin was 4.0%, Ethereum 5.31%, Binance 5.64%, and Ripple 4.83%. The results after normalization RMSE are Bitcoin 0.0062, Ethereum 0.063, Binance 0.073, and Ripple 0.055. The GRU Bi-LSTM hybrid model obtained very good results, yielding small RMSE results. After normalization, the results get closer to 0 and MAPE scores below 10% with RMSE.
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Romanuke, Vadim. "Maximum-versus-mean absolute error in selecting criteria of time series forecasting quality." Bionics of Intelligence 1, no. 96 (2021): 3–9. https://doi.org/10.30837/bi.2021.1(96).01.
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In time series forecasting, a commonly accepted criterion of the forecasting quality is the root-mean-square error (RMSE). Sometimes only RMSE is used. In other cases, another measure of forecasting accuracy is used along with RMSE. It is the mean absolute error (MAE). Although RMSE and MAE are the common criteria of time series forecasting quality, they both register information about averaged errors. However, averaging may remove information about volatility, which is typical for time series, in a few points (outliers) or narrow intervals. Information about outliers in time series forecasts (with respect to test data) can be registered by the maximum absolute error (MaxAE). The MaxAE criterion does not have any relation to averaging. It registers information about the worst outlier instead. Therefore, the goal is to ascertain the best criteria of time series forecasting quality, wherein the RMSE criterion is always present. First, 12 types of benchmark time series are defined to test and select criteria. The time series is of 168 points, whereas the last third of the series is forecasted. After having generated 200 times series for each of those 12 types, ARIMA forecasts are made at 56 points of every series. All the 2400 RMSEs are sorted in ascending order, whereupon the respective MAEs and MaxAEs are re-arranged as well. The interrelation between the RMSE and MAE/MaxAE is studied by their intercorrelation function. RMSEs and MaxAEs are “more different” than RMSEs and MAEs, because the correlation between the RMSE and MAE is stronger. Consequently, the MAE criterion is useless as it just nearly replicates information about the forecasting quality from the RMSE criterion. Inasmuch as the MaxAE criterion can import additional information about the forecasting quality, the best criteria are RMSE and MaxAE.
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พงษ์พานิช, บำรุงพงษ์, ศุภัคกุณ ชัยฤทธิ์, ทักษิณา สิทธิผล та วาสนา สุวรรณวิจิตร. "การพยากรณ์ปริมาณการส่งออกทุเรียนสดของไทยด้วยวิธีบอกซ์-เจนกินส์และวิธีปรับเรียบด้วยเส้นโค้งเลขชี้กำลังของวินเทอร์แบบบวก". Economics and Business Administration Journal Thaksin University 15, № 3 (2023): 1–16. http://dx.doi.org/10.55164/ecbajournal.v15i3.263235.
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การศึกษาครั้งนี้มีวัตถุประสงค์เพื่อสร้างและคัดเลือกตัวแบบพยากรณ์ที่เหมาะสมและเปรียบเทียบวิธีการพยากรณ์ปริมาณการส่งออกทุเรียนสดของไทย 2 วิธี ได้แก่ วิธีบอกซ์–เจนกินส์และวิธีปรับเรียบด้วยเส้นโค้งเลขชี้กำลังของวินเทอร์แบบบวก สำหรับการตรวจสอบความแม่นยำของการพยากรณ์พิจารณาจากค่าเฉลี่ยเปอร์เซ็นต์ความคลาดเคลื่อนสัมบูรณ์ (Mean Absolute Percentage Error: MAPE) และค่ารากที่สองของความคลาดเคลื่อนกำลังสอง (Root Mean Square Error: RMSE) โดยใช้ข้อมูลในการวิเคราะห์จากเว็บไซต์ของสำนักงานเศรษฐกิจการเกษตร ตั้งแต่เดือนมกราคม พ.ศ. 2554 ถึงเดือนธันวาคม พ.ศ. 2564 จำนวน 132 เดือน แบ่งข้อมูลออกเป็น 2 ชุด ชุดที่ 1 ตั้งแต่เดือนมกราคม พ.ศ.2554 ถึงเดือนธันวาคม พ.ศ.2563 จำนวน 120 เดือนสำหรับการสร้างตัวแบบพยากรณ์ ชุดที่ 2 ตั้งแต่เดือนมกราคม พ.ศ.2564 ถึงเดือนธันวาคม พ.ศ.2564 จำนวน 12 เดือนสำหรับทดสอบและเปรียบเทียบความแม่นยำของตัวแบบพยากรณ์ ผลการศึกษาพบว่าการพยากรณ์ด้วยวิธีปรับเรียบด้วยเส้นโค้งเลขชี้กำลังของวินเทอร์แบบบวกให้ค่า MAPE และค่า RMSE ต่ำกว่าวิธีบอกซ์-เจนกินส์ จึงเลือกตัวแบบดังกล่าวมาใช้ในการพยากรณ์ปริมาณการส่งออกทุเรียนสด
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Nurhaida, Ida, Mochamad Sobiri, and Safitri Jaya. "Optimasi Prediksi Cryptocurrency Menggunakan Pendekatan Deep Learning." JSAI (Journal Scientific and Applied Informatics) 6, no. 2 (2023): 197–204. http://dx.doi.org/10.36085/jsai.v6i2.5288.
Full textAbstract:
Cryptocurrency adalah mata uang digital terdesentralisasi yang diatur oleh pemerintah pusat. Karena cryptocurrency sangat fluktuatif, analisis diperlukan sebelum menggunakan cryptocurrency untuk meminimalkan kerugian. Penelitian ini melakukan perbandingan antara model Long Short Term Memory (LSTM) dan algoritma optimasi seperti Adam dan Root Mean Square Propagation (RMSProp) untuk melakukan prediksi terhadap nilai cryptocurrency. Metode LSTM dioptimasi menggunakan Adam Optimizer dan dievaluasi berdasarkan Root Mean Square Error (RMSE). Dengan demikian diperoleh prediksi nilai RMSE sebesar 0.08217562639465784 yang merupakan nilai error yang kecil sehingga mendekati nilai aktual. Sedangkan nilai RMSE 0.10699215580552895 menggunakan RMSProp mendapatkan nilai yang lebih besar yang berdampak terhadap akurasi hasil prediksi. Dengan demikian kombinasi antara algoritma LSTM dan Adam dapat melakukan prediksi dan mengoptimasi data dengan akurat.