Thermal coal futures trading volume predictions through the neural network

Journal of Modelling in Management, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 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.

Language: Английский

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Forecasts of coking coal futures price indices through Gaussian process regressions

Mineral Economics, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 17, 2024

Language: Английский

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Machine learning price index forecasts of flat steel products

Mineral Economics, Journal Year: 2024, Volume and Issue: unknown

Published: July 22, 2024

Language: Английский

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Peanut oil price change forecasts through the neural network

foresight, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Purpose For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one essential edible oil, peanut oil’s price swings are certainly important to predict. In this paper, the weekly wholesale index for period January 1, 2010 10, 2020 used address specific challenge Chinese market. Design/methodology/approach The nonlinear auto-regressive neural network (NAR-NN) model method used. Forecasting performance based on various settings, such as training techniques, delay counts, hidden neuron counts and data segmentation ratios, assessed build final specification. Findings With training, validation testing root mean square errors 5.89, 4.96 5.57, respectively, produces reliable accurate forecasts. Here, paper demonstrates applicability NAR-NN approach predictions. Originality/value On hand, findings may be independent technical movement Conversely, they included forecast combinations with forecasts derived from other models form viewpoints patterns policy research.

Language: Английский

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Predicting Scrap Steel Prices Through Machine Learning for South China

Materials Circular Economy, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 6, 2025

Language: Английский

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Machine learning gold price forecasting

International Journal of Management Science and Engineering Management, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: Jan. 21, 2025

Language: Английский

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Predicting open interest in thermal coal futures using machine learning

Mineral Economics, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 23, 2024

Language: Английский

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Prediction of FeO content in sintered ore based on ICEEMDAN and CNN-BiLSTM-AM

Ironmaking & Steelmaking Processes Products and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

FeO content of sintered ore is an important reference index for measuring the performance ore. It significantly impacts ironmaking process, iron quality, and energy consumption. Aiming at current problem delayed poor accuracy detection results, this article proposes a hybrid network model that incorporates improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), convolutional neural (CNN), bidirectional long short-term memory (BiLSTM), attention mechanism (AM) prediction. First, time series were decomposed using ICEEMDAN to obtain sub-layers different frequencies. Then, features higher correlation selected by feature selection as inputs, followed predicting sequences CNN-BiLSTM-AM feature-selected variables, respectively. Finally, all predicted sublayer predictions reconstructed into final prediction summation. The proposed effectively captures essence sequence through algorithm, extracts deep from data CNN, contextual information BiLSTM, enhances extraction capability AM. experimental results show collaboration AM modelling improves accuracy. Additionally, algorithm employed enhance further, offering advantages over other techniques. MAE, MAPE, RMSE, RRMSE, R² new ICEEMDAN-CNN-BiLSTM-AM (ICBA) are 0.0751, 0.846%, 0.0937, 1.0500%, 0.9646, respectively, demonstrating significant improvement in outperforming relevant comparison models.

Language: Английский

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

Quality & Quantity, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Language: Английский

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Machine learning platinum price predictions

The Engineering Economist, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 27

Published: Feb. 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.

Language: Английский

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