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.

Many market participants have historically placed a great deal of importance on price projections for energy commodities. To tackle the problem, our analysis looks at Henry Hub natural gas prices on a monthly basis. The examined sample covers almost 30 years, from January 1997 to March 2024, and this price series has significant economic implications. In this case, price projections are generated through the use of Gaussian process regression techniques, which are obtained through the application of cross-validation processes and Bayesian optimization approaches. A relative root mean square error of 3.1744% indicates that our empirical prediction method produces reasonably accurate price estimates for the out-of-sample testing phase of November 2018–March 2024. Models for predicting prices give investors and governments the information they need to make wise choices about the natural gas market.

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.

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.