Quality & Quantity, Год журнала: 2025, Номер unknown
Опубликована: Март 6, 2025
Язык: Английский
Asian Journal of Economics and Banking, Год журнала: 2024, Номер unknown
Опубликована: Май 23, 2024
Purpose Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale index green grams in Chinese market. covers ten-year period, from January 1, 2010, 3, 2020, and has significant economic implications. Design/methodology/approach In order nonlinear patterns present time series, investigate auto-regressive neural network as forecast model. This modeling technique is able combine basic functions approximate more complex characteristics. Specifically, examine prediction performance that corresponds several configurations across data splitting ratios, hidden neuron delay counts, model estimation approaches. Findings Our turns out be rather simple yields with good stability accuracy. Relative root mean square errors throughout training, validation testing are specifically 4.34, 4.71 3.98%, respectively. results benchmark research show produces statistically considerably better when compared other machine learning models classic time-series econometric methods. Originality/value Utilizing our findings independent technical would one use. Alternatively, policy fresh insights into might achieved combining them (basic) outputs.
Язык: Английский
Процитировано
104
Ironmaking & Steelmaking Processes Products and Applications, Год журнала: 2024, Номер unknown
Опубликована: Май 16, 2024
From 1 January 2010 to 15 April 2021, this study examines the challenging task of daily regional steel price index forecasting in east Chinese market. We train our models using cross-validation and Bayesian optimisations implemented through expected improvement per second plus algorithm, utilise Gaussian process regressions validate findings. Investigated parameters as part model training involve predictor standardisation status, basis functions, kernels standard deviation noises. The that were built accurately predicted indices between 8 2019 with an out-of-sample relative root mean square error 0.57%, 0.84, absolute 0.48, correlation coefficient 99.81%.
Язык: Английский
Процитировано
68
Journal of Modelling in Management, Год журнала: 2024, Номер unknown
Опубликована: Июнь 4, 2024
Purpose The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in last 10 years, which an important concern both government and investors. Design/methodology/approach This examines Gaussian process regressions with different kernels basis functions monthly pre-owned index estimates ten major cities from March 2012 May 2020. authors do by using Bayesian optimizations cross-validation. Findings indices June 2019 2020 are accurately predicted out-of-sample established models, have relative root mean square errors ranging 0.0458% 0.3035% correlation coefficients 93.9160% 99.9653%. Originality/value results might be applied separately or conjunction other develop hypotheses regarding patterns residential real estate conduct further policy research.
Язык: Английский
Процитировано
67
Ironmaking & Steelmaking Processes Products and Applications, Год журнала: 2024, Номер 51(6), С. 515 - 526
Опубликована: Май 5, 2024
The problem of dynamic relationships among the price indices 10 major steel products – rebar, wire, plate, hot rolled coil, cold galvannealed sheet, seamless tube, welded section and narrow strip is addressed in present work for Chinese market from 2011M7 to 2021M4. For examination contemporaneous causal links series, we use data on a daily basis combine vector error correction model directed acyclic graph. This analysis done using both Peter Clark linear non-Gaussian algorithms. With exception series each part cointegration according model, all save thin strips respond long-run equilibrium disturbances. method allows us achieve routes that allow innovation accounting, but algorithm prevented reaching an We categorise complex dynamics adjustment processes after shocks based impulsive responses, which tube are predominating comparison other seven items. Our findings show these three goods should get most consideration when designing long-term strategies prices.
Язык: Английский
Процитировано
61
Materials Circular Economy, Год журнала: 2024, Номер 6(1)
Опубликована: Июнь 11, 2024
Язык: Английский
Процитировано
52
Neural Computing and Applications, Год журнала: 2024, Номер 36(33), С. 20863 - 20882
Опубликована: Авг. 19, 2024
Язык: Английский
Процитировано
52
Ironmaking & Steelmaking Processes Products and Applications, Год журнала: 2024, Номер 51(8), С. 819 - 834
Опубликована: Июль 23, 2024
Given thermal coal's significance as a tactical energy source, price projections for the commodity are crucial investors and decision-makers alike. The goal of current work is to determine whether Gaussian process regressions useful this forecast problem using dataset closing prices coal traded on China Zhengzhou Commodity Exchange from January 4, 2016, December 31, 2020. This significant financial index that has not received enough attention in literature terms forecasting. Our forecasting exercises make use Bayesian optimizations cross-validation. 02, 2020, 2020 successfully predicted by generated models, with out-of-sample relative root mean square error 0.4210%. shown be problem. outcomes projection might used independent technical forecasts or conjunction other policy research entails developing viewpoints patterns.
Язык: Английский
Процитировано
36
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
Процитировано
33
Deleted Journal, Год журнала: 2024, Номер 01
Опубликована: Янв. 1, 2024
Energy index price forecasting has long been a crucial undertaking for investors and regulators. This study examines the daily predicting problem new energy on Chinese mainland market from January 4, 2016 to December 31, 2020 as insufficient attention paid in literature this financial metric. Gaussian process regressions facilitate our analysis, training procedures of models make use cross-validation Bayesian optimizations. From 2, 2020, was properly projected by created models, with an out-of-sample relative root mean square error 1.8837%. The developed may be utilized investors’ policymakers’ policy analysis decision-making processes. Because results provide reference information about patterns indicated they also useful building similar indices.
Язык: Английский
Процитировано
24
Mineral Economics, Год журнала: 2024, Номер unknown
Опубликована: Сен. 17, 2024
Язык: Английский
Процитировано
21