Narrow Results

Governments and investors have historically found faith in price forecasts for a broad variety of commodities. By using time-series data covering 23 August 2013–15 April 2021, this study investigates the complicated challenge of projecting scrap steel prices that are provided daily at the national level for China. Prior research has not given adequate consideration to estimates in this crucial evaluation of commodity prices. In this instance, Gaussian process regression algorithms are developed using cross-validation processes and Bayesian optimization approaches, leading to the construction of price forecasts. Our empirical prediction technique produces reasonably accurate price estimates for the out-of-sample period encompassing 17 September 2019–15 April 2021, with a relative root mean square error of 0.1053%. Governments and investors may utilize price prediction models to make educated decisions about the scrap steel industry.

Regulators and investors have always placed a high premium on commodity price forecasting. This study examines the weekly price forecast issue for the China commodities price index for the period from June 2 2006 to 17 January 2020. This important commodity price indicator’s forecasting has not received enough attention in the literature. We use cross-validation and Bayesian optimizations during model training, and our analysis is supported by Gaussian process regressions. With an out-of-sample relative root mean square error of 0.1334%, the created models correctly forecasted the price index between 6 January 2017 and 17 January 2020. The generated models can be used by policymakers and investors for policy analysis and decision-making. The forecasting findings might be helpful in creating similar commodity price indices based on reference data on the price trends projected by the models.

For a considerable amount of time, many market participants have placed great importance on price forecasts for major metal commodities. To tackle the problem, our study looks at the price of copper recorded on a daily basis. The sample under inquiry spans more than 10 years, from 01/02/2014 to 04/12/2024, and the price series under examination has substantial financial implications. In this instance, Gaussian process regression models are built using cross-validation techniques and Bayesian optimization methodologies, and the resultant strategies are employed to provide price estimations. The relative root-mean-square error of 1.3880% indicates that our empirical prediction approach produces reasonably accurate price estimates for the out-of-sample assessment period of 04/11/2022–04/12/2024. Models for predicting prices give investors and governments the information they require to make wise choices about the copper market.

The extraction of the structural features of materials is fundamental for investigating novel properties in fields such as electronic information and biochemistry. However, existing experimental methods have limitations in analyzing material structures with sufficient depth. Therefore, rapid and accurate extraction and analysis of structural features from atomic coordinates obtained through simulation calculations are crucial for advancing the exploration of new material properties. Herein, we propose an approach for extracting the structural features of materials by combining the holographic matrix method with Bayesian optimization and tensor flow operations. The proposed algorithm efficiently classifies and statistically analyzes cluster structures within materials. Experimental validation conducted on a system comprising 8000 atoms demonstrated a correct recognition rate exceeding 99.213%. Moreover, the algorithm achieved an average recognition time of approximately $1.81\pm 0.03$s. The proposed analytical framework exhibits scalability and robustness, establishing an algorithmic foundation for future advancements in big data analytics for complex materials.

Most learning algorithms require the practitioner to manually set the values of many hyperparameters before the learning process can begin. However, with modern algorithms, the evaluation of a given hyperparameter setting can take a considerable amount of time and the search space is often very high-dimensional. We suggest using a lower-dimensional representation of the original data to quickly identify promising areas in the hyperparameter space. This information can then be used to initialize the optimization algorithm for the original, higher-dimensional data. We compare this approach with the standard procedure of optimizing the hyperparameters only on the original input.

We perform experiments with various state-of-the-art hyperparameter optimization algorithms such as random search, the tree of parzen estimators (TPEs), sequential model-based algorithm configuration (SMAC), and a genetic algorithm (GA). Our experiments indicate that it is possible to speed up the optimization process by using lower-dimensional data representations at the beginning, while increasing the dimensionality of the input later in the optimization process. This is independent of the underlying optimization procedure, making the approach promising for many existing hyperparameter optimization algorithms.

A significant number of market participants have placed a high level of importance on price estimates for the primary metal commodities for a considerable amount of time. To tackle the problem, we investigate the daily reported price of silver in our study. The sample that is being analyzed spans a period of 13 years, starting on April 20, 2011, and ending on April 19, 2024. The price series which is being investigated has important implications for the commercial world. Specifically, when it comes to this unique circumstance, Gaussian process regression models are developed utilizing cross-validation strategies and Bayesian optimization procedures. The forecasting of prices is therefore accomplished via the use of the methods that are developed as a result of the situation. For the out-of-sample evaluation period that extends from October 5, 2021 to April 19, 2024, our empirical forecasting approach yields price estimates deemed reasonably accurate. The relative root mean square error reached for the silver price is 0.2257%, with the corresponding root mean square error of 0.0515, mean absolute error of 0.0389, and correlation coefficient of 99.967%. Due to the availability of models that forecast prices, investors and governments are supplied with the information they need to make educated judgments on the silver market by providing them with the knowledge they require. The framework of the Gaussian process regression with Bayesian optimizations demonstrates its good potential for modeling and forecasting sophisticated commodity price series for market participants.

Most market players have found great significance in price projections for basic agricultural commodities for a substantial duration. We look at the daily price of coffee that is released in this research in order to tackle the issue. The analytical sample runs from 2 January 2013 to 10 April 2024, a period of more than 12 years. A significant influence on the business sector comes from the price series under investigation. Specifically, in this particular situation, Gaussian process regression models are developed using Bayesian optimization techniques and cross-validation processes. Thus, this circumstance prompts the development of price forecasting methodologies. Using our empirical forecasting technique, we produce relatively accurate price projections for the out-of-sample assessment period, which runs from 3 January 2022 to 10 April 2024. It was found that price forecasts of coffee had a relative root mean square error of 2.0500%. With the availability of price forecasting models, investors and governments can make educated decisions about the coffee market given that they have access to the required data.

In recent years, a new demand has appeared for evaluations of earthquake fault displacements, to address the need to evaluate the soundness of underground structures. Fault displacements are caused by the rupturing of earthquake source faults, and are investigated through the use of methods such as the finite difference method and the finite element method (FEM). We conducted dynamic rupture simulations on the Kamishiro Fault Earthquake using a nonlinear FEM, focused on time history of fault displacement and response displacement, and demonstrated an ability to simulate observed values to a certain extent. During these simulations, we created models of homogeneous faults using the ground as the solid element and fault planes as joint elements. Although we were able to roughly simulate displacement time histories, obstacles to achieving more precise simulations still exist. In this research, we conducted investigations to model strong motion generation areas (SMGA). We conducted a searching analysis using Bayesian optimization with SMGA distribution within faults as parameters, and estimated the optimal parameters for simulating time histories of displacement. In addition, we compared our results with estimations of SMGA derived from different methods, and demonstrated that our distributions qualitatively matched. In addition, we evaluated the stochasticity of response displacement considering the randomness of the parameter of the fault. To conduct the simulation, we introduced joint elements from Goodman et al. that had been expanded to the FEM code FrontISTR, which makes it possible to analyze large-scale models.

Energy commodity price forecasts have always been quite important to a lot of market participants. To tackle the problem, our analysis looks at West Texas Intermediate (WTI) crude oil prices on a daily basis. The sample under inquiry covers 10 years, from April 4, 2014 to April 3, 2024, and the price series under analysis has major financial implications. Here, Gaussian process regression methods are developed using Bayesian optimization techniques and cross-validation processes, and the resulting strategies are utilized to provide price projections. The relative root mean square error of 2.2743% indicates that our empirical prediction approach produces reasonably accurate price estimates for the out-of-sample assessment period of April 19, 2022–April 3, 2024. Models of price predictions give investors and governments the information they need to make wise choices about the crude oil market.

Designing new materials with desired properties is a complex and time-consuming process. One of the most challenging factors of the design process is the huge search space of possible materials. Machine learning methods such as $k$-nearest neighbors, support vector machine (SVM) and artificial neural network (ANN) can contribute to this process by predicting materials properties accurately. Properties of multi-principal element alloys (MPEAs) highly depend on alloys’ phase. Thus, accurate prediction of the alloy’s phase is important to narrow down the search space. In this paper, we propose a solution of employing SVM method with hyperparameters tuning and the use of weighted values for prediction of the alloy’s phase. Using the dataset consisting of the experimental results of 118 MPEAs, our solution achieves a cross-validation accuracy of 90.2%. We confirm the superiority of this score over the performance of ANN statistically. On the other dataset containing 401 MPEAs, our SVM model is comparable to ANN and exhibits 70.6% cross-validation accuracy. We also found that additional variables, including average melting temperature and standard deviation of melting temperature, increase prediction accuracy by 3.34% in the best case.

Forecasts regarding the prices of energy commodities have long been significant to many market players. Our research examines the price of Brent crude oil on a daily basis in order to address the issue. The price series under investigation has significant financial ramifications, and the sample under investigation spans 10 years, from April 7, 2014 to March 28, 2024. In this case, cross-validation procedures and Bayesian optimization approaches are used to construct Gaussian process regression methods, and the resulting strategies are used to generate price estimates. For the out-of-sample evaluation period of March 24, 2022 to March 28, 2024, our empirical prediction technique yields relatively accurate projections of prices, as indicated by the relative root mean square error of 0.2814%. Price prediction models provide governments and investors with the knowledge they need to make informed decisions regarding the crude oil market.

Forecasts of prices for a wide range of commodities have been a source of confidence for governments and investors throughout history. This study examines the difficult task of forecasting scrap steel prices, which are released every day for the southwest China market, leveraging time-series data spanning August 23, 2013 to April 15, 2021. Estimates have not been fully considered in previous studies for this important commodity price assessment. In this case, cross-validation procedures and Bayesian optimization techniques are used to develop Gaussian process regression strategies, and consequent price projections are built. Arriving at a relative root mean square error of 0.4691%, this empirical prediction approach yields fairly precise price projections throughout the out-of-sample stage spanning September 17, 2019 to April 15, 2021. Through the use of price research models, governments and investors may make well-informed judgments on regional markets of scrap steel.

Throughout history, governments and investors have relied on predictions of prices for a broad spectrum of commodities. Using time-series data covering 08/23/2013–04/15/2021, this study investigates the challenging problem of predicting scrap steel prices, which are issued daily for the northeast China market. Previous research has not sufficiently taken into account estimates for this significant commodity price measurement. In this instance, Gaussian process regression methods are created using Bayesian optimisation approaches and cross-validation processes, and the resulting price forecasts are constructed. This empirical prediction methodology provides reasonably accurate price estimates for the out-of-sample period from 09/17/2019 to 04/15/2021, with a root mean square error of 9.6951, mean absolute error of 5.4218, and correlation coefficient of 99.9122%. Governments and investors can arrive at informed decisions regarding regional scrap steel markets by using pricing research models.

Energy index price forecasting has long been a crucial undertaking for investors and regulators. This study examines the daily price predicting problem for the new energy index on the Chinese mainland market from January 4, 2016 to December 31, 2020 as insufficient attention has been paid to price forecasting in the literature for this crucial financial metric. Gaussian process regressions facilitate our analysis, and training procedures of the models make use of cross-validation and Bayesian optimizations. From January 2, 2020 to December 31, 2020, the price was properly projected by the created models, with an out-of-sample relative root mean square error of 1.8837%. The developed models may be utilized in investors’ and policymakers’ policy analysis and decision-making processes. Because the forecasting results provide reference information about price patterns indicated by the models, they may also be useful in building of similar energy indices.

RF circuit optimization is generally formed as a multi-objective optimization function. Weight setting is important can be guided by a principal component analysis (PCA) step that can also be used to screen parameters. This is illustrated on a circuit example. Two surrogate model-based optimization approaches are presented, one based on Bayesian Optimization, the second on a candidate search method. Bayesian optimization is best employed on all continuous domain problems while candidate search can be used on mixed integer-continuous problems. We implement co-Kriging in the candidate search algorithm to permit mixed fidelity electromagnetic evaluation. These algorithms are illustrated with several circuit examples.

Acute infection, if not rapidly and accurately detected, can lead to sepsis, organ failure and even death. Current detection of acute infection as well as assessment of a patient’s severity of illness are imperfect. Characterization of a patient’s immune response by quantifying expression levels of specific genes from blood represents a potentially more timely and precise means of accomplishing both tasks. Machine learning methods provide a platform to leverage this host response for development of deployment-ready classification models. Prioritization of promising classifiers is dependent, in part, on hyperparameter optimization for which a number of approaches including grid search, random sampling and Bayesian optimization have been shown to be effective. We compare HO approaches for the development of diagnostic classifiers of acute infection and in-hospital mortality from gene expression of 29 diagnostic markers. We take a deployment-centered approach to our comprehensive analysis, accounting for heterogeneity in our multi-study patient cohort with our choices of dataset partitioning and hyperparameter optimization objective as well as assessing selected classifiers in external (as well as internal) validation. We find that classifiers selected by Bayesian optimization for in-hospital mortality can outperform those selected by grid search or random sampling. However, in contrast to previous research: 1) Bayesian optimization is not more efficient in selecting classifiers in all instances compared to grid search or random sampling-based methods and 2) we note marginal gains in classifier performance in only specific circumstances when using a common variant of Bayesian optimization (i.e. automatic relevance determination). Our analysis highlights the need for further practical, deployment-centered benchmarking of HO approaches in the healthcare context.