Thermal coal futures trading volume predictions through the neural network

Journal of Modelling in Management, Год журнала: 2024, Номер unknown

Опубликована: Сен. 16, 2024

Purpose Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose this study is concentrate on the energy sector explore volume prediction issue for thermal coal traded in Zhengzhou Commodity Exchange China with daily data spanning January 2016–December 2020. Design/methodology/approach nonlinear autoregressive neural network adopted performance examined based upon variety settings over algorithms model estimations, numbers hidden neurons delays ratios splitting series into training, validation testing phases. Findings A relatively simple setting arrived at that leads predictions good accuracy stabilities maintains small errors up 99.273 th quantile observed volume. Originality/value results could, one hand, serve as standalone technical predictions. They other be combined different (fundamental) forming perspectives trends carrying out policy analysis.

Язык: Английский

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Forecasts of coking coal futures price indices through Gaussian process regressions

Mineral Economics, Год журнала: 2024, Номер unknown

Опубликована: Сен. 17, 2024

Язык: Английский

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Predicting Scrap Steel Prices Through Machine Learning for South China

Materials Circular Economy, Год журнала: 2025, Номер 7(1)

Опубликована: Янв. 6, 2025

Язык: Английский

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Machine learning gold price forecasting

International Journal of Management Science and Engineering Management, Год журнала: 2025, Номер unknown, С. 1 - 13

Опубликована: Янв. 21, 2025

Язык: Английский

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Machine learning platinum price predictions

The Engineering Economist, Год журнала: 2025, Номер unknown, С. 1 - 27

Опубликована: Фев. 12, 2025

Throughout history, governments and investors have placed trust in price predictions for a wide range of commodities. This research explores the complex problem forecasting daily platinum prices United States using time-series data spanning from January 02, 1969 to March 15, 2024. Estimates not received enough attention previous studies this important assessment commodity pricing. Here, projections are created by Gaussian process regression algorithms that estimated with use cross-validation procedures Bayesian optimization techniques. Arriving at relative root mean square error 1.5486%, our empirical prediction method yields relatively precise out-of-sample phase covering 04/03/2013–03/15/2024. Price models can be used make informed decisions regarding business.

Язык: Английский

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Predicting open interest in thermal coal futures using machine learning

Mineral Economics, Год журнала: 2024, Номер unknown

Опубликована: Окт. 23, 2024

Язык: Английский

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Machine learning Brent crude oil price forecasts

Innovation and Emerging Technologies, Год журнала: 2024, Номер 11

Опубликована: Янв. 1, 2024

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

Язык: Английский

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Predictions of residential property price indices for China via machine learning models

Quality & Quantity, Год журнала: 2025, Номер unknown

Опубликована: Фев. 11, 2025

Язык: Английский

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Scrap steel price predictions for southwest China via machine learning

Innovation and Emerging Technologies, Год журнала: 2025, Номер 12

Опубликована: Янв. 1, 2025

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

Язык: Английский

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Rental price index forecasts of residential properties using Gaussian process regressions

Journal of Financial Management of Property and Construction, Год журнала: 2025, Номер unknown

Опубликована: Март 27, 2025

Purpose Since the Chinese real estate market has expanded so quickly over past 10 years, investors and government are both quite concerned about projecting future property prices. Design/methodology/approach This work aims to investigate monthly rental price index forecasts of residential properties for ten major cities from 3M2012 5M2020 by using Gaussian process regressions with a diverse variety kernels basis functions. The authors conduct forecast exercises through use Bayesian optimizations cross-validation. Findings With relative root mean square errors spanning range 0.0370%–0.8953%, constructed models successfully indices 6M2019 out sample. Originality/value findings might be used independently or in combination other projections create theories trends carry additional policy analysis.

Язык: Английский

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