Relevant bibliographies by topics / Mean square errors

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Mean square errors'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Mean square errors"

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Hodson, Timothy O. "Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not." Geoscientific Model Development 15, no. 14 (July 19, 2022): 5481–87. http://dx.doi.org/10.5194/gmd-15-5481-2022.

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

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When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean absolute error (MAE) are more interpretable than MSE or RMSE. Part of that historical preference for sums-of-squares measures is that they are mathematically amenable to decomposition and one can then form ratios, such as those based on separating MSE into its systematic and unsystematic components. Here, we develop and illustrate a decomposition of MAE into three useful submeasures: (1) bias error, (2) proportionality error, and (3) unsystematic error. This three-part decomposition of MAE is preferable to comparable decompositions of MSE because it provides more straightforward information on the nature of the model-error distribution. We illustrate the properties of our new three-part decomposition using a long-term reconstruction of streamflow for the Upper Colorado River.
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Kishimoto, K., K. Onaga, and E. Nakamae. "Theoretical assessment of mean square errors of antialiasing filters." Computer Vision, Graphics, and Image Processing 35, no. 2 (August 1986): 277. http://dx.doi.org/10.1016/0734-189x(86)90043-5.

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Kishimoto, K., K. Onaga, and E. Nakamae. "Theoretical assessments of mean square errors of antialiasing filters." Computer Vision, Graphics, and Image Processing 37, no. 3 (March 1987): 428–37. http://dx.doi.org/10.1016/0734-189x(87)90047-8.

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Clements, Michael P., and David F. Hendry. "On the limitations of comparing mean square forecast errors." Journal of Forecasting 12, no. 8 (December 1993): 617–37. http://dx.doi.org/10.1002/for.3980120802.

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Ogasawara, Haruhiko. "Optimal Information Criteria Minimizing Their Asymptotic Mean Square Errors." Sankhya B 78, no. 1 (March 10, 2016): 152–82. http://dx.doi.org/10.1007/s13571-016-0115-9.

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Jadhav, Smita, and Dipika Jaspal. "Adsorptive eradication of tartrazine from aqueous solutions onto doped polyaniline." Journal of the Serbian Chemical Society 85, no. 2 (2020): 251–63. http://dx.doi.org/10.2298/jsc190705116j.

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A potential polymeric adsorbent, doped polyaniline (PANI) has been investigated for the eradication of the hazardous dye tartrazine from textile effluent. During the adsorption process, the influence of the acidic character of the adsorbate, pH, dose of the adsorbent, dye concentration and time of contact between the adsorbent and adsorbate were evaluated. The outcomes attained from batch experiments were applied to the Langmuir and the Freundlich isothermal models. Different error analysis techniques, such as mean square error, root mean square error, the Chi-square test (?2), sum of absolute errors and sum of squared errors, were determined for the doped polyaniline?tartrazine system. The Langmuir isotherm was established as the best-fit isothermal model, with minimum errors and high regression values. About 90?97 % removal was achieved in the first 70 min. A positive enthalpy value implied the adsorption process was endothermic. The energy of activation for the dye adsorbent system was found to be 28.9 kJ mol-1, which is in line with physisorption.
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Nakonechnyi, Alexander, Grigoriy Kudin, Taras Zinko, and Petr Zinko. "MINIMAX ROOT–MEAN–SQUARE ESTIMATES OF MATRIX PARAMETERS IN LINEAR REGRESSION PROBLEMS UNDER UNCERTAINTY." Journal of Automation and Information sciences 4 (July 1, 2021): 28–37. http://dx.doi.org/10.34229/1028-0979-2021-4-3.

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The issues of parameter estimation in linear regression problems with random matrix coefficients were researched. Given that random linear functions are observed from unknown matrices with random errors that have unknown correlation matrices, the problems of guaranteed mean square estimation of linear functions of matrices were investigated. The estimates of the upper and lower guaranteed standard errors of linear estimates of observations of linear functions of matrices were obtained in the case when the sets are found, for which the unknown matrices and correlation matrices of observation errors are known. It was proved that for some partial cases such estimates are accurate. Assuming that the sets are bounded, convex and closed, more accurate two-sided estimates have been gained for guaranteed errors. The conditions when the guaranteed mean squared errors approach zero as the number of observations increases were found. The necessary and sufficient conditions for the unbiasedness of linear estimates of linear functions of matrices were provided. The notion of quasi-optimal estimates for linear functions of matrices was introduced, and it was proved that in the class of unbiased estimates, quasi-optimal estimates exist and are unique. For such estimates, the conditions of convergence to zero of the guaranteed mean-square errors were obtained. Also, for linear estimates of unknown matrices, the concept of quasi-minimax estimates was introduced and it was confirmed that they are unbiased. For special sets, which include an unknown matrix and correlation matrices of observation errors, such estimates were expressed through the solution of linear operator equations in a finite-dimensional space. For quasi-minimax estimates under certain assumptions, the form of the guaranteed mean squared error of the unknown matrix was found. It was shown that such errors are limited by the sum of traces of the known matrices. An example of finding a minimax unbiased linear estimation was given for a special type of random matrices that are included in the observation equation.
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Anh, Nguyen Dong, and Nguyen Minh Triet. "A FULL DUAL MEAN SQUARE ERROR CRITERION FOR THE EQUIVALENT LINEARIZATION." Vietnam Journal of Science and Technology 54, no. 4 (August 18, 2016): 557. http://dx.doi.org/10.15625/0866-708x/54/4/8103.

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Among approximate methods, the method of equivalent linearization proposed by N. Krylov and N. Bogoliubov and extended by Caughey has remained an effective tool for both deterministic and stochastic problems. The idea of the method is based on the replacement of a nonlinear oscillator by a linear one under the same excitation. The standard way of implementing this method is that the coefficients of linearization are to be found from a criterion of equivalence. When the difference between the nonlinear function and equivalent linear one is significant the replacement leads to unaccepted errors. In order to reduce the errors one may apply the dual approach. One of significant advantages of the dual conception is its consideration of two different aspects of a problem in question allowing the investigation to be more appropriate. In this paper a special case of the weighted full dual mean square error criterion is introduced and investigated in detail. Numerical results are carried out to show that this special full dual mean square error criterion can give more accurate approximate solutions for both deterministic and random nonlinear systems.
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Ogasawara, Haruhiko. "Predictive estimation of variances and covariances using mean square errors." Proceedings of the Annual Convention of the Japanese Psychological Association 83 (September 11, 2019): 1A—039–1A—039. http://dx.doi.org/10.4992/pacjpa.83.0_1a-039.

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Dissertations / Theses on the topic "Mean square errors"

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Ponce, Hector F. "Is It More Advantageous to Administer Libqual+® Lite Over Libqual+®? an Analysis of Confidence Intervals, Root Mean Square Errors, and Bias." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc283825/.

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The Association of Research Libraries (ARL) provides an option for librarians to administer a combination of LibQUAL+® and LibQUAL+® Lite to measure users' perceptions of library service quality. LibQUAL+® Lite is a shorter version of LibQUAL+® that uses planned missing data in its design. The present study investigates the loss of information in commonly administered proportions of LibQUAL+® and LibQUAL+® Lite when compared to administering LibQUAL+® alone. Data from previous administrations of LibQUAL+® protocol (2005, N = 525; 2007, N = 3,261; and 2009, N = 2,103) were used to create simulated datasets representing various proportions of LibQUAL+® versus LibQUAL+® Lite administration (0.2:0.8, 0.4:0.6. 0.5:0.5, 0.6:0.4, and 0.8:0.2). Statistics (i.e., means, adequacy and superiority gaps, standard deviations, Pearson product-moment correlation coefficients, and polychoric correlation coefficients) from simulated and real data were compared. Confidence intervals captured the original values. Root mean square errors and absolute and relative biases of correlations showed that accuracy in the estimates decreased with increase in percentage of planned missing data. The recommendation is to avoid using combinations with more than 20% planned missing data.
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Ainkaran, Ponnuthurai. "Analysis of Some Linear and Nonlinear Time Series Models." Thesis, The University of Sydney, 2004. http://hdl.handle.net/2123/582.

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Abstract This thesis considers some linear and nonlinear time series models. In the linear case, the analysis of a large number of short time series generated by a first order autoregressive type model is considered. The conditional and exact maximum likelihood procedures are developed to estimate parameters. Simulation results are presented and compare the bias and the mean square errors of the parameter estimates. In Chapter 3, five important nonlinear models are considered and their time series properties are discussed. The estimating function approach for nonlinear models is developed in detail in Chapter 4 and examples are added to illustrate the theory. A simulation study is carried out to examine the finite sample behavior of these proposed estimates based on the estimating functions.
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Ainkaran, Ponnuthurai. "Analysis of Some Linear and Nonlinear Time Series Models." University of Sydney. Mathematics & statistics, 2004. http://hdl.handle.net/2123/582.

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Abstract This thesis considers some linear and nonlinear time series models. In the linear case, the analysis of a large number of short time series generated by a first order autoregressive type model is considered. The conditional and exact maximum likelihood procedures are developed to estimate parameters. Simulation results are presented and compare the bias and the mean square errors of the parameter estimates. In Chapter 3, five important nonlinear models are considered and their time series properties are discussed. The estimating function approach for nonlinear models is developed in detail in Chapter 4 and examples are added to illustrate the theory. A simulation study is carried out to examine the finite sample behavior of these proposed estimates based on the estimating functions.
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Degtyarena, Anna Semenovna. "The window least mean square error algorithm." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2385.

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In order to improve the performance of LMS (least mean square) algorithm by decreasing the amount of calculations this research proposes to make an update on each step only for those elements from the input data set, that fall within a small window W near the separating hyperplane surface. This work aims to describe in detail the results that can be achieved by using the proposed LMS with window learning algorithm in information systems that employ the methodology of neural network for the purposes of classification.
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Cui, Xiangchen. "Mean-Square Error Bounds and Perfect Sampling for Conditional Coding." DigitalCommons@USU, 2000. https://digitalcommons.usu.edu/etd/7107.

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In this dissertation, new theoretical results are obtained for bounding convergence and mean-square error in conditional coding. Further new statistical methods for the practical application of conditional coding are developed. Criteria for the uniform convergence are first examined. Conditional coding Markov chains are aperiodic, π-irreducible, and Harris recurrent. By applying the general theories of uniform ergodicity of Markov chains on genera l state space, one can conclude that conditional coding Markov cha ins are uniformly ergodic and further, theoretical convergence rates based on Doeblin's condition can be found. Conditional coding Markov chains can be also viewed as having finite state space. This allows use of techniques to get bounds on the second largest eigenvalue which lead to bounds on convergence rate and the mean-square error of sample averages. The results are applied in two examples showing that these bounds are useful in practice. Next some algorithms for perfect sampling in conditional coding are studied. An application of exact sampling to the independence sampler is shown to be equivalent to standard rejection sampling. In case of single-site updating, traditional perfect sampling is not directly applicable when the state space has large cardinality and is not stochastically ordered, so a new procedure is developed that gives perfect samples at a predetermined confidence interval. In last chapter procedures and possibilities of applying conditional coding to mixture models are explored. Conditional coding can be used for analysis of a finite mixture model. This methodology is general and easy to use.
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Strobel, Matthias. "Estimation of minimum mean squared error with variable metric from censored observations." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-35333.

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Fodor, Balázs [Verfasser]. "Contributions to Statistical Modeling for Minimum Mean Square Error Estimation in Speech Enhancement / Balázs Fodor." Aachen : Shaker, 2015. http://d-nb.info/1070151815/34.

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Xing, Chengwen, and 邢成文. "Linear minimum mean-square-error transceiver design for amplify-and-forward multiple antenna relaying systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44769738.

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Nicolson, Aaron M. "Deep Learning for Minimum Mean-Square Error and Missing Data Approaches to Robust Speech Processing." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/399974.

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Speech corrupted by background noise (or noisy speech) can cause misinterpretation and fatigue during phone and conference calls, and for hearing aid users. Noisy speech can also severely impact the performance of speech processing systems such as automatic speech recognition (ASR), automatic speaker verification (ASV), and automatic speaker identification (ASI) systems. Currently, deep learning approaches are employed in an end-to-end fashion to improve robustness. The target speech (or clean speech) is used as the training target or large noisy speech datasets are used to facilitate multi-condition training. In this dissertation, we propose competitive alternatives to the preceding approaches by updating two classic robust speech processing techniques using deep learning. The two techniques include minimum mean-square error (MMSE) and missing data approaches. An MMSE estimator aims to improve the perceived quality and intelligibility of noisy speech. This is accomplished by suppressing any background noise without distorting the speech. Prior to the introduction of deep learning, MMSE estimators were the standard speech enhancement approach. MMSE estimators require the accurate estimation of the a priori signal-to-noise ratio (SNR) to attain a high level of speech enhancement performance. However, current methods produce a priori SNR estimates with a large tracking delay and a considerable amount of bias. Hence, we propose a deep learning approach to a priori SNR estimation that is significantly more accurate than previous estimators, called Deep Xi. Through objective and subjective testing across multiple conditions, such as real-world non-stationary and coloured noise sources at multiple SNR levels, we show that Deep Xi allows MMSE estimators to produce the highest quality enhanced speech amongst all clean speech magnitude spectrum estimators. Missing data approaches improve robustness by performing inference only on noisy speech features that reliably represent clean speech. In particular, the marginalisation method was able to significantly increase the robustness of Gaussian mixture model (GMM)-based speech classification systems (e.g. GMM-based ASR, ASV, or ASI systems) in the early 2000s. However, deep neural networks (DNNs) used in current speech classification systems are non-probabilistic, a requirement for marginalisation. Hence, multi-condition training or noisy speech pre-processing is used to increase the robustness of DNN-based speech classification systems. Recently, sum-product networks (SPNs) were proposed, which are deep probabilistic graphical models that can perform the probabilistic queries required for missing data approaches. While available toolkits for SPNs are in their infancy, we show through an ASI task that SPNs using missing data approaches could be a strong alternative for robust speech processing in the future. This dissertation demonstrates that MMSE estimators and missing data approaches are still relevant approaches to robust speech processing when assisted by deep learning.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Eng & Built Env
Science, Environment, Engineering and Technology
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Dear, K. B. G. "A generalisation of mean squared error and its application to variance component estimation." Thesis, University of Reading, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379691.

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Books on the topic "Mean square errors"

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Gaver, Donald Paul. Bayesian prediction of mean square errors with covariates. Monterey, Calif: Naval Postgraduate School, 1992.

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Patterson, K. D. Revisions to the national income accounts: A multiplicative decomposition of mean square errors. Reading: University of Reading. Department of Economics, 1990.

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Clements, Michael P. On the limitations of comparing mean square forecast error. Oxford: Oxford University, Institute of Economics and Statistics, 1992.

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Baram, Yoram. Mean-square error bounds for reduced-order linear state estimators. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1987.

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Hoque, Asraul. The exact multiperiod mean-square forecast error for the first-order autoregressive model. London: London School of Economics, 1986.

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Clark, Todd E. Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis. Kansas City [Mo.]: Research Division, Federal Reserve Bank of Kansas City, 2004.

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Magnus, Jan R. The exact multiperiod mean-square forecast error for the first-order autoregressive modelwith an intercept. London: National Institute of Economic and Social Research, 1988.

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Magnus, Jan R. The exact multiperiod mean-square forecast error for the first-order autoregressive model with an intercept. London: International Centre for Economics and Related Disciplines, 1988.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., ed. Measured and predicted root-mean-square errors in square and triangular antenna mesh facets. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1989.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., ed. Measured and predicted root-mean-square errors in square and triangular antenna mesh facets. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1989.

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Book chapters on the topic "Mean square errors"

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Theil, Henri, and Myron Scholes. "Forecast Evaluation Based on a Multiplicative Decomposition of Mean Square Errors." In Advanced Studies in Theoretical and Applied Econometrics, 1153–73. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2410-2_8.

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Mean Squared Error." In Encyclopedia of Machine Learning, 653. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_528.

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Shekhar, Shashi, and Hui Xiong. "Root-Mean-Square Error." In Encyclopedia of GIS, 979. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1142.

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Benesty, Jacob, Jingdong Chen, Yiteng Huang, and Israel Cohen. "Mean-Squared Error Criterion." In Noise Reduction in Speech Processing, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00296-0_4.

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Weik, Martin H. "minimum mean-square error filtering." In Computer Science and Communications Dictionary, 1022. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_11569.

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Weik, Martin H. "minimum mean-square error restoration." In Computer Science and Communications Dictionary, 1022. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_11570.

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Principe, Jose C., Yadunandana N. Rao, and Deniz Erdogmus. "Error Whitening Wiener Filters: Theory and Algorithms." In Least-Mean-Square Adaptive Filters, 445–89. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471461288.ch10.

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Dekking, Frederik Michel, Cornelis Kraaikamp, Hendrik Paul Lopuhaä, and Ludolf Erwin Meester. "Efficiency and mean squared error." In A Modern Introduction to Probability and Statistics, 299–311. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-168-7_20.

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Morales, Domingo, María Dolores Esteban, Agustín Pérez, and Tomáš Hobza. "Mean Squared Error of EBLUPs." In A Course on Small Area Estimation and Mixed Models, 209–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63757-6_9.

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Liu, Yan. "Mean Square Error of Survey Estimates." In Encyclopedia of Quality of Life and Well-Being Research, 3892–93. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_1754.

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Conference papers on the topic "Mean square errors"

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Skorohod, B. "Study of Mean Square Errors of Receding Horizon Unbiased FIR Filters." In 2020 International Russian Automation Conference (RusAutoCon). IEEE, 2020. http://dx.doi.org/10.1109/rusautocon49822.2020.9208092.

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Ihn, Yong Seok, Sae Whan Park, and Ja Choon Koo. "Position Alignment of Micro Manipulator Using Root Mean Square Errors for Maskless Lithography System." In ASME 2013 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/isps2013-2943.

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A precision optical exposure process of lithography has become one of the essential processes to manufacture and develop micro electro mechanical system (MEMS) devices, flat panel display (FPD), and semiconductor. For a typical exposure process for the lithography, a photomask that is often very expensive is required to generate patterns[1]. So it is very inefficient not only in terms of cost but also the time due to the reliance on the photomasks in development. The alternative solution is the maskless lithography system, which does not use photomasks since the patterns are generated by using a digital mirror device (DMD). The unit mirror of a digital mirror device has two kinds of status which are on and off, then the status of unit mirrors configures a pattern called point array method. The maskless lithography system can reduce the amount of work forces significantly and save money as well. However, the maskless lithography system has a critical drawback, which is a low throughput since the patterns are generated in a line. This is an intrinsic problem of point array method. So in most volume production processes of maskless lithography system, a number of optical heads are used in order to maximize throughput of the expositing process. And to guarantee the exposure quality, multiple numbers of optical heads should be accurately aligned to each other, and then the focal plane of each optical head is also well aligned with chuck.
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Liao, Y. Y., H. N. Chiang, J. J. Wang, F. C. Hsu, C. F. Wu, and Shreyes N. Melkote. "A Part Error Model Considering the Machine Tool Positioning Errors and Process-Induced Errors." In ASME 2009 International Manufacturing Science and Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/msec2009-84179.

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Parts geometrical and dimensional error for a machining process can be attributed to several factors, including tool wear, thermal deformation, the machine tool positioning error and force-induced process error. Although the latter two factors are often more significant, their effect on the parts accuracy is more elusive and difficult to predict due to their inherent statistical dispersion property. It is therefore the subject of this investigation to quantitatively relate the parts error to machine tool spatial error and process-induced errors. Through root mean square calculation, a part error model is established by combining the machine tool positioning error, work vibration and tool vibration. The part error model considers two ranges of surface error consisting of surface roughness and cutting depth error of a machined plate. Using milling process as an example, the part error is predicted and compared with measurement result. The validity of this model is verified through a series of milling experiments under various cutting conditions.
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Bielecki, Mark F., Jason J. Kemper, and Thomas L. Acker. "A Methodology for Comprehensive Characterization of Errors in Wind Power Forecasting." In ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90381.

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Wind power forecasting will play a more important role in electrical system planning with the greater wind penetrations of the coming decades. Wind will most likely comprise a larger percentage of the generation mix, and as a result forecasting errors may have more significant effects on balancing operations. The natural uncertainties associated with wind along with limitations in numerical weather prediction (NWP) models lead to these forecasting errors, which play a considerable role in the impacts and costs of utility-scale wind integration. The premise of this project was to examine errors between the actual and commercially forecasted power production data from a typical wind power plant in the Northwestern United States. An exhaustive statistical characterization of the forecast behavior and error trends was undertaken, which allowed the most important metrics for describing wind power forecast errors to be identified. This paper presents only the metrics considered by the authors to be most significant. While basic information about wind forecast accuracy such as the mean absolute error (MAE) is valuable, a more detailed description is useful for system operators or in wind integration studies. System planners have expressed major concern in the area of forecast performance during large wind ramping events. For such reasons, this methodology included the development of a comprehensive ramp identification algorithm to select significant ramp events from the data record, and particular attention was paid to the error analysis during these events. The algorithm allows user input to select ramps of any desired magnitude, and also performs correlation analysis between forecasted ramp events and actual ramp events that coincide within a desired timing window. From this procedure, an investigation of the magnitude and phase of forecast errors was conducted for various forecast horizons. The metrics found to be of most importance for error characterization were selected based on overall impacts, and were ranked in a rudimentary (and perhaps subjective) order of significance. These metrics included: mean absolute error, root mean square error, average magnitude of step changes, standard deviation of step changes, mean bias levels, correlation coefficient of power values, mean temporal bias of ramp events, and others. While these metrics were selected and the methodology was developed for a single dataset, the entire process can be applied generally to any wind power and forecast time series. The implications for such a process include use for generating a synthetic wind power forecast for wind integration studies that will reproduce the same error trends as those found in a real forecast.
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ÜNEŞ, Fatih, Bestami TAŞAR, Hakan VARÇİN, and Ercan GEMİCİ. ""River Sediment Amounts Prediction with Regression and Support Vector Machine Methods."." In Air and Water – Components of the Environment 2022 Conference Proceedings. Casa Cărţii de Ştiinţă, 2022. http://dx.doi.org/10.24193/awc2022_10.

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Accurate estimation of the amount of sediment in rivers; determination of pollution, river transport, determination of dam life, etc. matters are very important. In this study, sediment estimation in the river was made using Interaction Regression (IR), Pure-Quadratic Regression (PQR) and Support Vector machine (SVM) methods. The observation station on the Patapsco River near Catonsville was chosen as the study area. Prediction model was developed by using daily flow and turbidity data between 2015- 2018 as input parameters. Models were compared to each other according to three statistical criteria, namely, root mean square errors (RMSE), mean absolute relative error (MAE) and determination coefficient (R2 ). These criteria were used to evaluate the performance of the models. When the model results were compared with each other, it was seen that the IR model gave results consistent with the actual measurement results.
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MOZGERIS, Gintautas, Ina BIKUVIENĖ, and Donatas JONIKAVICIUS. "THE OPPORTUNITIES AND CHALLENGES OF USING AIRBORNE LASER SCANNING FOR FOREST INVENTORIES IN LITHUANIA." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.023.

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The aim of this study was to test the usability of airborne laser scanning (ALS) data for stand-wise forest inventories in Lithuania based on operational approaches from Nordic countries, taking into account Lithuanian forest conditions and requirements for stand-wise inventories, such as more complex forests, unified requirements for inventory of all forests, i.e. no matter the ownership, availability of supporting material from previous inventories and high accuracy requirements for total volume estimation. Test area in central part of Lithuania (area 2674 ha) was scanned using target point density 1 m-2 followed by measurements of 440 circular field plots (area 100–500 m2). Detailed information on 22 final felling areas with all trees callipered (total area 42.7 ha) was made available to represent forest at mature age. Updated information from conventional stand-wise inventory was made available for the whole study area, too. A two phase sampling with nonparametric Most Similar Neighbor estimator was used to predict point-wise forest characteristics. Total volume of the stand per 1 ha was predicted with an root mean square error of 18.6 %, basal area – 17.7 %, mean diameter – 13.6 %, mean height – 7.9 % and number of tree – 42.8 % at plot-level with practically no significant bias. However, the relative root mean square errors increased 2–4 times when trying to predict forest characteristics by three major groups of tree species – pine, spruce and all deciduous trees taken together. Main conclusion of the study was that accuracy of predicting volume using ALS data decreased notably when targeting forest characteristics by three major groups of tree species.
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Clarke, Stephen. "Objective assessment of forecasting assignments using some function of prediction errors." In Assessing Student leaning in Statistics. International Association for Statistical Education, 2007. http://dx.doi.org/10.52041/srap.07501.

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One way of examining forecasting methods via assignments is to give each student a real or simulated set of data, with a requirement to forecast future values. However checking the accuracy of calculations for the host of possible methods can be onerous. One solution is to make part or the entire assessment dependent on the accuracy of the forecasts obtained. This mirrors real life, where forecasts are judged not by the method used but by how accurate the predictions turn out. This paper investigates how this might work with an actual example. Using simulated data from a model which incorporates trend, seasonality, Easter effect and randomness, we use a function of the mean square error of the forecasts to determine the final mark for a variety of methods. Results indicate that the students who have put in more work, and/or fitted the better models, would obtain the better marks.
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Vegendla, Prasad, Rui Hu, and Ling Zou. "CFD Model Validation for a Benchmark Data of Texas A&M 1/16th Scaled VHTR Upper Plenum." In ASME 2020 Verification and Validation Symposium. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/vvs2020-8813.

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Abstract In High Temperature Gas-cooled Reactors (HTGR), gas flow patterns are very complex and reduced order models (1D or 2D) may be too simplified to predict accurate reactor performance. 3D Computational Fluid Dynamics (CFD) models can help provide the detailed information needed to optimize the reactor thermal performance. The main objective of this work is to validate the CFD models with data of a 1/16th scaled Very High Temperature Reactor (VHTR) upper plenum measured at Texas A&M University. In this paper, the flow characteristics of a single isothermal jet discharging into the upper plenum was investigated using Nek5000 Large-Eddy Simulation (LES) CFD tool. Several numerical simulations were performed for various jet Reynolds numbers ranging from 3,413 to 12,819. Grid independent study was performed. The numerical results of mean velocity, root-mean-square fluctuating velocity, and Reynolds stress were validated with the benchmark data. Good agreement was obtained between simulated and measured data for axial mean velocities, except near the upper plenum hemisphere. The maximum predicted errors for axial mean velocities at various normalized coolant channel diameter heights of 1, 5 and 10 are 1.56%, 1.88% and 3.82%, respectively. Also, the predicted root-mean-square fluctuating velocity and Reynolds stress are qualitatively agreed with the experimental data.
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Guo, Yongwei, Qiming Wang, and Xuedong Gu. "Analysis of Manufacture Errors for Space Truss With Bolted Spherical Joints Base on Monte Carlo Method." In ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-25180.

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The active reflector of the five-hundred-meter aperture sphere radio telescope (FAST) is constituted by about 4600 triangular reflector elements, whose surface error will determine the telescope’s antenna efficiency. One solution is a grid structure backup with the bolted spherical joints, in which the surface accuracy is dominated mainly by the length accuracy of the rods in it. In this paper, an accuracy analysis based on Monte Carlo method is proceeded to predict the surface error. Random errors are input the rods, and the surface errors in root mean square (rms) are output. After the calculations, the final accuracy’s probability distribution charts are given for the different error inputs. And a relationship chart between rod length error and final accuracy is given to help determining the most reasonable rod length error. To solve the position of the grid structure, an iteration method is deduced. The relationship between joints and rods in the frame structure which is described as a graph, is presented by adjacency list. Forces on joints are balanced at the beginning, but when rod length errors are input, forces to pull or push the joints are yielded. The process of moving their positions to make the forces balance again is the process to solve the surface accuracy. An existing reflector element model was measured. The measurement verified the analysis. Finally, the proper accuracy of rods length error is given. It is useful for cost controlling and time keeping in FAST design, for the possibility to achieve a qualified surface by controlling the manufacture error of rods reveals that the reflector elements maybe avoid adjustment and can be mounted to the telescope directly. The method to predict accuracy can be generalized for the other grid structures.
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Hellström, Erik, Anna Stefanopoulou, and Li Jiang. "A Linear Least-Squares Algorithm for Double-Wiebe Functions Applied to Spark-Assisted Compression Ignition." In ASME 2013 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icef2013-19068.

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An algorithm for determining the four tuning parameters in a double-Wiebe description of the combustion process in spark-assisted compression ignition engines is presented where the novelty is that the tuning problem is posed as a weighted linear least-squares problem. The approach is applied and shown to describe well an extensive data set from a light-duty gasoline engine for various engine speeds and loads. Correlations are suggested for the four parameters based on the results, which illustrates how the double-Wiebe approach can also be utilized in predictive simulation. The effectiveness of the methodology is quantified by the accuracy for describing and predicting the heat release rate as well as predicting the cylinder pressure. The root-mean square errors between the measured and predicted cylinder pressures are 1 bar or less, which corresponds to 2% or less of the peak cylinder pressure.
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Reports on the topic "Mean square errors"

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Gaver, Donald P., and Patricia A. Jacobs. Bayesian Prediction of Mean Square Errors with Covariates. Fort Belvoir, VA: Defense Technical Information Center, November 1992. http://dx.doi.org/10.21236/ada259585.

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Collins, Clarence O., and Tyler J. Hesser. altWIZ : A System for Satellite Radar Altimeter Evaluation of Modeled Wave Heights. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39699.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the design and implementation of a wave model evaluation system, altWIZ, which uses wave height observations from operational satellite radar altimeters. The altWIZ system utilizes two recently released altimeter databases: Ribal and Young (2019) and European Space Agency Sea State Climate Change Initiative v.1.1 level 2 (Dodet et al. 2020). The system facilitates model evaluation against 1 Hz1 altimeter data or a product created by averaging altimeter data in space and time around model grid points. The system allows, for the first time, quantitative analysis of spatial model errors within the U.S. Army Corps of Engineers (USACE) Wave Information Study (WIS) 30+ year hindcast for coastal United States. The system is demonstrated on the WIS 2017 Atlantic hindcast, using a 1/2° basin scale grid and a 1/4° regional grid of the East Coast. Consistent spatial patterns of increased bias and root-mean-square-error are exposed. Seasonal strengthening and weakening of these spatial patterns are found, related to the seasonal variation of wave energy. Some model errors correspond to areas known for high currents, and thus wave-current interaction. In conjunction with the model comparison, additional functions for pairing altimeter measurements with buoy data and storm tracks have been built. Appendices give information on the code access (Appendix I), organization and files (Appendix II), example usage (Appendix III), and demonstrating options (Appendix IV).
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Clark, Todd, and Kenneth West. Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference. Cambridge, MA: National Bureau of Economic Research, January 2005. http://dx.doi.org/10.3386/t0305.

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Sun, Winston Y. Linear adaptive noise-reduction filters for tomographic imaging: Optimizing for minimum mean square error. Office of Scientific and Technical Information (OSTI), April 1993. http://dx.doi.org/10.2172/10148667.

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Lio, Y. L., and W. J. Padgett. On the Mean Squared Error of Nonparametric Quantile Estimators under Random Right-Censorship. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada174517.

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Kwesiga, Victoria, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Compliance to GMP guidelines for Herbal Manufacturers in East Africa: A Position Paper. Purdue University, November 2021. http://dx.doi.org/10.5703/1288284317428.

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With the global increase in the use of traditional and complementary remedies for the prevention and treatment of illness, the quality and safety of these medicines have become a significant concern for all regulatory authorities. Herbal medicines are the most commonly used form of traditional and complementary medicines in the world and the efficacy and safety of herbal medicines, like conventional medicines, largely depends on their quality from planting to harvesting, preprocessing and final processing. Due to the inherent complexity of herbal medicines, often containing an array of active compounds, the primary processing of herbal medicines has a direct influence on their quality. Quality concerns are the reason why the medicines regulatory agencies insist that manufacturers of medicines strictly follow Good Manufacturing Practices since it is an essential tool to prevent instances of contamination, mix-ups, deviations, failures and errors. However, a strict application of GMP requirements is expensive and would drive the prices of the manufactured products up. As a result, a maturity level grading of facilities is proposed as a way of justifying the costs incurred for manufacturers desiring to reach a broader market and investing in continuous improvement. 36 Good Manufacturing Practice (GMP) inspection reports of local herbal manufacturers conducted by National Drug Authority were analyzed to establish the type and extent of deficiencies to GMP requirements for local herbal manufacturers in Uganda. The different GMP chapters and related sub-parameters constituted the variables used for the analysis of conformity to requirements. The primary outcome variable was the conclusion regarding compliance or noncompliance of the inspected local herbal manufacturing facility. GMP parameters that were frequently defaulted by local herbal manufacturers and the corresponding frequencies were identified. The Pearson Chi-square test was applied independently on each category to find the association that existed between conformity and the questions in each category. Only 22% (8) of the 30 inspected facilities were found to comply with GMP requirements, as per National Drug Authority (NDA) guidelines; while the majority of the facilities, 28 (78%), were found not to comply. Of the facilities inspected, 25 were undergoing GMP inspection for the first time. A total of 1,236 deficiency observations were made in the 36 inspection reports reviewed for the study. The mean for all deficiencies was 34.3, and the standard deviation was 15.829. 91.5% of the facilities did not have mechanisms for a record of market complaints; 80.9% did not meet documentation requirements; 78.9% did not have quality control measures in place, and 65.7% did not meet stores requirements. By encouraging a culture of self/voluntary improvement through the introduction of listing of manufacturers based on a maturity level grading, the National Drug Authority will improve the Herbal Medicines sector as per the mandate of improving the herbal medicine industry. Also, increased sensitization of all relevant stakeholders regarding the requirements for GMP should be intensified.
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Bruder, Brittany L., Katherine L. Brodie, Tyler J. Hesser, Nicholas J. Spore, Matthew W. Farthing, and Alexander D. Renaud. guiBath y : A Graphical User Interface to Estimate Nearshore Bathymetry from Hovering Unmanned Aerial System Imagery. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39700.

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This US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, technical report details guiBathy, a graphical user interface to estimate nearshore bathymetry from imagery collected via a hovering Unmanned Aerial System (UAS). guiBathy provides an end-to-end solution for non-subject-matter-experts to utilize commercia-off-the-shelf UAS to collect quantitative imagery of the nearshore by packaging robust photogrammetric and signal-processing algorithms into an easy-to-use software interface. This report begins by providing brief background on coastal imaging and the photogrammetry and bathymetric inversion algorithms guiBathy utilizes, as well as UAS data collection requirements. The report then describes guiBathy software specifications, features, and workflow. Example guiBathy applications conclude the report with UAS bathymetry measurements taken during the 2020 Atlantic Hurricane Season, which compare favorably (root mean square error = 0.44 to 0.72 m; bias = -0.35 to -0.11 m) with in situ survey measurements. guiBathy is a standalone executable software for Windows 10 platforms and will be freely available at www.github.com/erdc.
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Pradhan, Nawa Raj. Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42128.

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A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.
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Rossi, Jose Luiz, Carlos Piccioni, Marina Rossi, and Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004488.

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We investigated the predictability of the Brazilian exchange rate at High Frequency (1, 5 and 15 minutes), using local and global economic variables as predictors. In addition to the Linear Regression method, we use Machine Learning algorithms such as Ridge, Lasso, Elastic Net, Random Forest and Gradient Boosting. When considering contemporary predictors, it is possible to outperform the Random Walk at all frequencies, with local economic variables having greater predictive power than global ones. Machine Learning methods are also capable of reducing the mean squared error. When we consider only lagged predictors, it is possible to beat the Random Walk if we also consider the Brazilian Real futures as an additional predictor, for the frequency of one minute and up to two minutes ahead, confirming the importance of the Brazilian futures market in determining the spot exchange rate.
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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.
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