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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.

Due to the rapid growth of the Chinese housing market over the past ten years, forecasting home prices has become a crucial issue for investors and authorities alike. In this research, utilising Bayesian optimisations and cross validation, we investigate Gaussian process regressions across various kernels and basis functions for monthly residential real estate price index projections for ten significant Chinese cities from July 2005 to April 2021. The developed models provide accurate out-of-sample forecasts for the ten price indices from May 2019 to April 2021, with relative root mean square errors varying from 0.0207% to 0.2818%. Our findings could be used individually or in combination with other projections to formulate theories about the trends in the residential real estate price index and carry out additional policy analysis.

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.

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.