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Journal articles on the topic 'Rooted Mean Square Error (RMSE)'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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 observe

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Chicco, Davide, Matthijs J. Warrens, and Giuseppe Jurman. "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation." PeerJ Computer Science 7 (July 5, 2021): e623. http://dx.doi.org/10.7717/peerj-cs.623.

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Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of t

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Saminu, Umar, and M. Oyeyemi Gafar. "Evaluating the Forecast Accuracy of MGARCH Models and LSTM Networks for Multivariate Financial Time Series." International Journal of Novel Research in Physics Chemistry & Mathematics 12, no. 2 (2025): 1–8. https://doi.org/10.5281/zenodo.15449496.

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<strong>Abstract:</strong> Forecasting financial time series is a fundamental challenge in finance and econometrics, largely due to the complexity of volatility dynamics and interdependencies among assets. This study evaluates and compares the forecasting performance of MGARCH models, BEKK GARCH and DCC GARCH, with two deep learning networks, Single LSTM and BiLSTM, across short, medium and long-term forecast horizons. Two datasets, comprising simulated data and bank stock data were used. Forecast accuracy was assessed using Root Mean Squared Error (RMSE) on both simulated data and real-world

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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.

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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 i

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Hadi, Suyono, Santoso Hari, Nur Hasanah Rini, Wibawa Unggul, and Musirin Ismail. "Prediction of Solar Radiation Intensity using Extreme Learning Machine." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 691–98. https://doi.org/10.11591/ijeecs.v12.i2.pp691-698.

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The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This

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Sugondo, Hadiyoso, Nugroho Heru, Latifah Erawati Rajab Tati, and Surendro Kridanto. "Data prediction for cases of incorrect data in multi-node electrocardiogram monitoring." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1540–47. https://doi.org/10.11591/ijece.v12i2.pp1540-1547.

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The development of a mesh topology in multi-node electrocardiogram (ECG) monitoring based on the ZigBee protocol still has limitations. When more than one active ECG node sends a data stream, there will be incorrect data or damage due to a failure of synchronization. The incorrect data will affect signal interpretation. Therefore, a mechanism is needed to correct or predict the damaged data. In this study, the method of expectationmaximization (EM) and regression imputation (RI) was proposed to overcome these problems. Real data from previous studies are the main modalities used in this study.

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Liu, Wanyue, Jiaguo Li, Ying Zhang, Limin Zhao, and Qiuming Cheng. "Preflight Radiometric Calibration of TIS Sensor Onboard SDG-1 Satellite and Estimation of Its LST Retrieval Ability." Remote Sensing 13, no. 16 (2021): 3242. http://dx.doi.org/10.3390/rs13163242.

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The thermal Infrared Spectrometer (TIS) is the thermal infrared (TIR) sensor on-board the first Sustainable Development Goals (SDG-1) satellite. The TIS data can potentially be used to support improved monitoring of ground conditions with high-spatial resolutions, so accurate radiometric calibration is required. A meticulous radiometric calibration was conducted on the prototype of TIS to test its ability to convert a raw digital number (DN) to at-aperture radiance. The initial maximum radiometric error was 2.19 K at 300 K for Band 1(B1) and the minimum radiometric error was 0.25 K at 300 K ro

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Sanaa, Hammad Dhahi, Hammad Dhahi Estqlal, Jawad Khadhim Ban, and Taha Ahmed Shaymaa. "Using support vector machine regression to reduce cloud security risks in developing countries." Using support vector machine regression to reduce cloud security risks in developing countries 30, no. 2 (2023): 1159–66. https://doi.org/10.11591/ijeecs.v30.i2.pp1159-1166.

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The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman's Rank correlation coef

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Egop, S.E. "Approximation of Rainfall Intensity-Duration Frequency for Bayelsa State, Nigeria." Journal of Water Resource Research and Development 8, no. 1 (2025): 20–28. https://doi.org/10.5281/zenodo.14626734.

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<em>One of the most often utilized methods in water resources engineering is the approximation of the Rainfall Intensity-Duration-Frequency (IDF) relationship. Establishing the rainfall intensity-duration-frequency model and curves for Bayelsa State, Nigeria, is the goal of this study. The Nigeria Meteorological Agency (NIMET) provided 32 years of rainfall data, which was sorted for frequency analysis. The IDF model was developed using storm durations of 5, 10, 30, 60, 120, 240, 360, and 720 minutes, as well as the corresponding frequency of recurrence. The IDF curves for this investigation we

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Egop, S.E. "Approximation of Rainfall Intensity-Duration Frequency for Delta State, Nigeria." Journal of Advances in Civil Engineering and Management 8, no. 1 (2025): 17–25. https://doi.org/10.5281/zenodo.14636159.

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<em>A crucial tool in water resources engineering is the approximation of the Rainfall Intensity-Duration-Frequency (IDF) relationship. Establishing the rainfall intensity-duration-frequency model and curves for Delta State, Nigeria, is the goal of this study. The Nigeria Meteorological Agency (NIMET) provided 32 years of rainfall data, which was sorted for frequency analysis. The IDF model was developed using storm durations of 5, 10, 30, 60, 120, 240, 360, and 720 minutes, as well as the corresponding frequency of recurrence. The IDF curves for this investigation were created using the gener

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Al-Qazzaz, Redha Ali, and Suhad A. Yousif. "High performance time series models using auto autoregressive integrated moving average." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 422–30. https://doi.org/10.11591/ijeecs.v27.i1.pp422-430.

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Forecasting techniques have received considerable interest from both researchers and academics because of the unique characteristics of businesses and their influence on several areas of the economy. Most academics utilize the autoregressive integrated moving average (ARIMA) approach to forecasting the future. However, researchers face challenges, such as analyzing the data and selecting the appropriate ARIMA parameters, especially with large datasets. This study investigates the use of the automatic ARIMA (Auto ARIMA) function for forecasting Brent oil prices. It demonstrates the benefits of

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Yao, Junchen. "Stock Prediction of Google based on ARIMA, XGBoost and LSTM." BCP Business & Management 44 (April 27, 2023): 414–21. http://dx.doi.org/10.54691/bcpbm.v44i.4850.

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In the recent years, google has become one of the most powerful companies in the world, due to its big market dominance. More and more people want to predict the stock price of google, however changes in the stock price are hard to find because they combine with social and economic development. Therefore, many different models which can be divided into traditional-based model, machine learning and deep learning models are designed to improve the accuracy of stock price prediction. This paper firstly compared three high-frequency used different models based on different aspects: autoregressive

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Ashwini, Kumari Puttaramaiah, and Geethanjali Purushothaman. "Ensemble of constraint handling techniques for PV parameter extraction using differential evolutionary algorithms." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (2022): 1645–53. https://doi.org/10.11591/ijpeds.v13.i3.pp1645-1653.

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The depletion of fossil fuels and rising environmental concerns have paved the way for the development of clean renewable energy sources. Photovoltaic (PV) cells are represented by electrical equivalent circuits. Finding the right circuit model parameters for PV cells is critical task. Estimating accurate parameters helps in better performance assessment, control, efficiency calculation and maximum power point tracking. This manuscript describes a new approach for obtaining PV system parameters using ensemble of constraint handling techniques (ECHT) with evolutionary algorithms (EA). Four dist

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Zainul, Abidin, Aryawiratama Nauval, Muttaqin Adharul, and Miyauchi Ryoichi. "Design of field programmable gate array-based data processing system for multi global positioning system receiver." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 3466–76. https://doi.org/10.11591/ijece.v12i4.pp3466-3476.

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A global positioning system (GPS) sensor is needed for a ballistic/moving object to do position tracking. In previous study, a multi GPS processing system was made using several microcontrollers and data processing cannot be done simultaneously. Therefore, it was considered as ineffective system. In this research, field programmable gate array (FPGA)-based data processing system for multi-GPS receiver was proposed. The proposed system was designed to reduce root mean square error (RMSE). There are two main processes in the proposed system which work in parallel, i.e. data parsing and data proc

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Yu, Chunming, Xin Jin, and Dongsheng Li. "Methods and strategies of precision marketing complex system for big data using recommendation algorithm." Journal of Computational Methods in Sciences and Engineering 25, no. 2 (2024): 1382–93. https://doi.org/10.1177/14727978241299637.

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This research aims to explore precision marketing methodologies and strategies within the domain of big data, with a specific focus on the recommendation algorithm. The initial phase involves an in-depth analysis of the system prerequisites for a user-centric personalized recommendation system rooted in big data. Following this, the study introduces the Latent Factor-Based Matrix Factorization Completion Based Hybrid Weighted Recommendation Method (LF-WMC). Moreover, considering the neighbor information set of customer and item, the above two prediction results are mixed to get a new predictio

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Eteje, S. O., and P. D. Oluyori. "Impact of different centroid means on the accuracy of orthometric height modelling by geometric geoid method." Internaltional Journal of Scientific Report 6, no. 4 (2020): 124–30. https://doi.org/10.18203/issn.2454-2156.IntJSciRep20201267.

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Background: Orthometric height, as well as geoid modelling using the geometric method, requires centroid computation. And this can be obtained using various models, as well as methods. These methods of centroid mean computation have impacts on the accuracy of the geoid model since the basis of the development of the theory of each centroid mean type is different. This paper presents the impact of different centroid means on the accuracy of orthometric height modelling by geometric geoid method. Methods: DGPS observation was carried out to obtain the coordinates and ellipsoidal heights of selec

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Zhu, Hongfei, Jorge Leandro, and Qing Lin. "Optimization of Artificial Neural Network (ANN) for Maximum Flood Inundation Forecasts." Water 13, no. 16 (2021): 2252. http://dx.doi.org/10.3390/w13162252.

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Flooding is the world’s most catastrophic natural event in terms of losses. The ability to forecast flood events is crucial for controlling the risk of flooding to society and the environment. Artificial neural networks (ANN) have been adopted in recent studies to provide fast flood inundation forecasts. In this paper, an existing ANN trained based on synthetic events was optimized in two directions: extending the training dataset with the use of hybrid dataset, and selection of the best training function based on six possible functions, namely conjugate gradient backpropagation with Fletcher–

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Nadia, Roosmalita Sari, Firdaus Mahmudy Wayan, Prasetya Wibawa Aji, and Sonalitha Elta. "Enabling External Factors for Inflation Rate Forecasting using Fuzzy Neural System." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2746–56. https://doi.org/10.11591/ijece.v7i5.pp2746-2756.

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Inflation is the tendency of increasing prices of goods in general and happens continuously. Indonesia's economy will decline if inflation is not controlled properly. To control the inflation rate required an inflation rate forecasting in Indonesia. The forecasting result will be used as information to the government in order to keep the inflation rate stable. This study proposes Fuzzy Neural System (FNS) to forecast the inflation rate. This study uses historical data and external factors as the parameters. The external factor using in this study is very important, which inflation rate is

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Lin, Shucheng, Yue Wang, Haocheng Wei, Xiaoyi Wang, and Zhong Wang. "Hybrid Method for Oil Price Prediction Based on Feature Selection and XGBOOST-LSTM." Energies 18, no. 9 (2025): 2246. https://doi.org/10.3390/en18092246.

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The accurate and stable prediction of crude oil prices holds significant value, providing insightful guidance for investors and decision-makers. The intricate interplay of factors influencing oil prices and the pronounced fluctuations present significant obstacles within the realm of oil price forecasting. This study introduces a novel hybrid model framework, distinct from the conventional methods, that integrates influencing factors for oil price prediction. First, using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) extract mode components from crude o

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Vinod, Babu Pusuluri, M. Prasad A, and K. Darimireddy Naresh. "Optimization of 5G Sub Band Antenna Design Using Machine Learning Techniques for WiFi & WiMAX Application." Indian Journal of Science and Technology 18, no. 2 (2025): 160–67. https://doi.org/10.17485/IJST/v18i2.3681.

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<strong>Objectives:</strong> This article presents the design of a 5G-n78 sub-band frequency antenna for WiMAX and WLAN applications, along with its design optimization using four supervised machine learning models: Polynomial Regression (PR), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The performance of the machine learning models is evaluated using the Root Mean Square Error (RMSE) metric for both the test and train datasets. Furthermore, the dimensions obtained from the most efficient model, XGBoost, are utilized for fabrication and verification pu

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Ballamudi, Satyanarayana. "Comparative Analysis of Machine Learning Models for Laptop Price Prediction An Evaluation of Linear Regression, Histogram Gradient Boosting, and XGBoost Approaches." International Journal of Robotics and Machine Learning Technologies 1, no. 1 (2025): 1–12. https://doi.org/10.55124/ijrml.v1i1.234.

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In the rapidly evolving landscape of technology-driven commerce, laptops have become indispensable for both personal and professional applications, with a vast array of models presenting varied specifications and features. The intricate interplay of hardware configurations and pricing frameworks underscores the necessity for robust predictive models that empower consumers and manufacturers to make well-informed choices. This study delves into the critical challenge of accurately forecasting laptop prices by evaluating three machine learning methodologies: Linear Regression (LR), Histogram Grad

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Rahmadani, Alfiandi Aulia Rahmadani, Yan Watequlis Syaifudin, Triana Fatmawati, Pramana Yoga Saputra, and Rokhimatul Wakhidah. "Data-Driven Predictions of Fish Production: Applying Regression Methods in Aquaculture." International Journal of Frontier Technology and Engineering 2, no. 2 (2024): 65–78. https://doi.org/10.33795/ijfte.v2i2.6174.

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In the context of the industrial revolution 4.0, the fisheries sector is experiencing significant technological advancements, enhancing the effectiveness and efficiency of aquaculture in Indonesia, which plays a crucial role in food security. Aquaculture, defined in various ways, includes the cultivation of fish in diverse environments such as fields, rice paddies, and marine settings, and is seen as a key driver of economic growth by the Ministry of Marine Affairs and Fisheries (KKP), particularly through new initiatives that focus on export-based fisheries cultivation and the development of

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Bondarovich, Andrei A., Egor Yu. Mordvin, Nikita M. Pochyomin, and Anatoly A. Lagutin. "Reconstruction of root zone soil moisture according to the data from passive microwave radiometer and machine learning in the arid steppe region of Southern Western Siberia." Acta Biologica Sibirica 9 (November 4, 2023): 805–29. https://doi.org/10.5281/zenodo.10061576.

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Our study focuses on reconstruction root zone soil moisture (RZSM) in the Kulunda plain, a representative dry steppe area in southern Western Siberia, using remote sensing data (RSD) and machine learning techniques. We employed modern machine learning methods with soil surface layer moisture data from the AMSR2 passive microwave radiometer as the primary predictor. Additionally, we incorporated data from local meteorological and soil hydrological stations, as well as gravity lysimeter data for 2015–2017. This choice of predictors was based on the extensive time series of continuous observation

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Chanintorn, Jittawiriyanukoon. "Evaluation of a Multiple Regression Model for Noisy and Missing Data." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (2018): 2220–29. https://doi.org/10.11591/ijece.v8i4.pp2220-2229.

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The standard data collection problems may involve noiseless data while on the other hand large organizations commonly experience noisy and missing data, probably concerning data collected from individuals. As noisy and missing data will be significantly worrisome for occasions of the vast data collection then the investigation of different filtering techniques for big data environment would be remarkable. A multiple regression model where big data is employed for experimenting will be presented. Approximation for datasets with noisy and missing data is also proposed. The statistical root mean

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Andriani, Fitri, Cholichul Hadi, and Pramesti Pradna Paramita. "Development and Validity of Fluid Intelligence Test Based on Cattle-Horn-Carrol Theory: A Pilot Project." INSAN Jurnal Psikologi dan Kesehatan Mental 1, no. 2 (2017): 76. http://dx.doi.org/10.20473/jpkm.v1i22016.76-84.

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This study aimed to examine the validity of the Fluid Intelligence Test, constructed based on the Cattel-Horn-Carroll theory. There were two sources of validity used in this study, which were evidence based on the internal structure and evidence based on relation with other variables. Sixty-four items have been composed and tested to 242 people. The data was analyzed using confirmatory factor analysis technique and correlations technique to examine test validity. The result of this study showed that the prepared model worked quite well in describing the narrow abilities of fluid intelligence,

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Egop, S.E. "Optimization of Flood Quantiles at Lokoja River Station." Journal of Water Resource Research and Development 8, no. 2 (2025): 10–19. https://doi.org/10.5281/zenodo.15005406.

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<em>The ultimate drive of this research is to choose the best-fitted probability distribution function that optimally approximates the historical time series of the Lokoja hydrologic station in the Niger/Benue River Basin in Nigeria. The National Inland Waterways Authority (NIWA) in Lokoja provided the flood statistics. The principles of flood frequency analysis (FFA) was applied in the prediction using the annual maximum series (AMS). The observed data were fitted into eight (8) probability distribution models, involving Normal (N2), Gumbel (EV1), two-parameter log-Normal (LN2), Gamma, Pearso

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Sambasivam, T., A. H. M. Din, M. H. Hamden, N. A. Zulkifli, and N. H. M. Adzmi. "INTERPRETATION OF CURRENT AND TIDAL PATTERN AT SELANGOR RIVER, KUALA SELANGOR." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W6-2022 (February 7, 2023): 303–10. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w6-2022-303-2023.

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Abstract. Tidal data has been used to determine long-term water level alterations. Tides are high-powered and notoriously hard to ascertain. Tidal analysis and prediction need substantial study with the requisite approaches, techniques, and tools in conjunction with weather parameters and natural calamities. TOtal TIde Solution (TOTIS) software, which is rooted in harmonic analysis, is one of the most often used software in Malaysia. This study aims to interpret the current circulation and tidal pattern at the Selangor River, Kuala Selangor, Selangor Darul Ehsan. A period of one-month data is

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Naseem, Asima, Asima Naseem, Saima Naseem, et al. "Machine Learning Integration in Computational Fluid Dynamics for Turbulence Modeling." European Journal of Applied Science, Engineering and Technology 3, no. 1 (2025): 17–29. https://doi.org/10.59324/ejaset.2025.3(1).02.

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This study explores the integration of Machine Learning (ML) with Computational Fluid Dynamics (CFD) to improve turbulence modeling. Key findings indicate that ML-enhanced turbulence models significantly improve accuracy and computational efficiency compared to traditional models. High-fidelity turbulence data, including Direct Numerical Simulations (DNS), enable ML models to capture the chaotic and unsteady nature of flow fields with reduced Root Mean Square Error (RMSE). Additionally, integrating ML into frameworks such as Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES

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Ren, Weiwei, Zhongzheng Zhu, Yingzheng Wang, et al. "Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia." Remote Sensing 16, no. 6 (2024): 956. http://dx.doi.org/10.3390/rs16060956.

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Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on glacier dynamics. Since physical models often face challenges in comprehensively accounting for factors influencing glacial melt and uncertainties in inputs, machine learning (ML) offers a viable alternative due to its robust flexibility and nonlinear fitting capability. However, the effectiveness of ML in modeling GMB data across diverse glacier types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating ML models used f

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Vijaya, Kanaparthi. "Robustness Evaluation of LSTM-based Deep Learning Models for Bitcoin Price Prediction in the Presence of Random Disturbances." International Journal of Innovative Science and Modern Engineering (IJISME) 12, no. 2 (2024): 14–23. https://doi.org/10.35940/ijisme.B1313.12020224.

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<strong>Abstract:</strong> As Deep Learning (DL) continues to be widely adopted, the growing field of study on the robustness of DL approaches in finance is gaining steam. This paper investigates the robustness of a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) intended for daily closing price predictions of Bitcoin (BTC). The research entails reproducing and adjusting an LSTM design from previous research, with an emphasis on evaluating the robustness of the network. The network is trained using data that has been disturbed by Gaussian noise to assess robustness, and the e

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Besnard, Simon, Sujan Koirala, Maurizio Santoro, et al. "Mapping global forest age from forest inventories, biomass and climate data." Earth System Science Data 13, no. 10 (2021): 4881–96. http://dx.doi.org/10.5194/essd-13-4881-2021.

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Abstract. Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a rel

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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 (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 t

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Yuan, Yuan, Fengchen Fu, Yaling Li, et al. "Research and Application of Intelligent Weather Push Model Based on Travel Forecast and 5G Message." Atmosphere 14, no. 11 (2023): 1658. http://dx.doi.org/10.3390/atmos14111658.

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In the realm of daily activity planning, precise weather forecasting services hold paramount significance. However, the prevalent dissemination of weather forecasts through conventional channels like radio, television, and the internet often yields only generalized regional predictions. This limitation contributes to diminished forecast reach, inadequate accuracy, and a lack of individualization, thwarting the effective distribution of meteorological insights and inhibiting the fulfillment of personalized forecast demands. Addressing these concerns, our study proposes a personalized weather fo

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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 va

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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 MA

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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

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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

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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 da

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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.

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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

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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 e

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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 Squa

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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 perfo

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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 squar

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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 dilakuka

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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 ef

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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 ตั้ง

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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.

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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 0.08

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Purnama, Dian Normalitasari, Samsul Hadi, Sukirno, Heri Retnawati, and Rizki Nor Amelia. "Which one is more accurate, BILOG or R program? (a comparison for score test equating)." International Journal of Evaluation and Research in Education (IJERE) 13, no. 3 (2024): 1444–54. https://doi.org/10.11591/ijere.v13i3.26689.

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Evaluation may be carried out using different tests that are not necessarily parallel. Students with lower abilities may get higher scores while those with higher abilities get lower scores. Measurement errors caused by this condition require test equating. Several computer programs, including Bilog and the R program, can be used for test equating. Each program has a different level of accuracy, and the accuracy of the equating results will affect the standard errors of equating. This study aimed to find out the most accurate equating test method and the accuracy of the estimated BILOG and R p

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Yunkanabilla, Syalsia Fatiha, Ahmad Faisol, and Franciscus Xaverius Ariwibisono. "SISTEM PERAMALAN STOK PENJUALAN OBAT PERTANIAN BERBASIS WEB DENGAN PENDEKATAN REGRESI LINIER." Jurnal Informatika Teknologi dan Sains (Jinteks) 7, no. 1 (2025): 63–71. https://doi.org/10.51401/jinteks.v7i1.5063.

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Toko obat pertanian menduduki peran penting terhadap sektor pertanian dalam mendukung kebutuhan obat pertanian. Saat ini usaha Toko Pertanian Borneo masih belum memiliki sistem untuk memprediksi stok obat pertanian. Sehingga toko pertanian saat ini menghadapi kondisi peningkatan permintaan obat yang belum dapat dipenuhi oleh toko pertanian. Penelitian yang akan dilakukan ini merupakan penelitian dengan menerapkan metode Regresi Linier Berganda untuk dapat memprediksi stok obat pertanian dengan RMSE (Root Mean Square Error) berdasarkan parameter yang digunakan dalam proses perhitungan adalah mu

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