Coal Price Forecast Based on ARIMA Model

Zhang, Xiaofan; Liu, Chao; Qian, Yuhang

doi:10.18282/ff.v9i4.1530

Cited by 4 publications

(2 citation statements)

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“…Given the dominant position of coal in China's energy market, fluctuating coal prices not only determine the survival of coal enterprises but also directly affects economic growth, energy security and industrial raw material supply (Li and Lin, 2017;Jiang et al, 2018;Wang et al, 2020;Zhang et al, 2020;Liu, 2021). Therefore, this paper combines economic time series with production scheduling optimization for the first time.…”

Section: Price Time Series Prediction Methodsmentioning

confidence: 99%

Dynamic optimization of open-pit coal mine production scheduling based on ARIMA and fuzzy structured element

Liu

Yang

et al. 2023

Front. Earth Sci.

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A dynamic optimization method was created to address the production schedule issue in an open-pit coal mine while taking into account the characteristics of the fuzzy structured element. The fuzzy mining capacities of all “geologically optimal push-back bodies” were then examined using the moving cove method. One of the most crucial elements in the process of open-pit coal mine production scheduling optimization is coal pricing. As a result, this work also presents a dynamic optimization technique for production scheduling that incorporates the prediction of economic time series and the generation of dynamic economic indices. An appropriate time series model is created to forecast the future coal price based on previous data on coal prices. The prediction results are used in the calculation of optimal mining body generation to dynamically obtain the optimal production scheduling model. The Baorixile Open-pit Coal Mine in China’s Inner Mongolia Autonomous Region is using this method. The Autoregressive Integrated Moving Average Model ARIMA is constructed to anticipate the coal price in the future 23 years by evaluating and processing the coal price from 2009 to 2022, and the ideal production scheduling scheme of the mine economics is afterwards identified. The ideal fuzzy coal mining volume, the potential production life, and the fuzzy total net present value (NPV) of the annual production scheduling are all provided at the same time. The optimization findings can better give fundamental support for mine design and future production since the fuzzy problem is accurately expressed by correct formulations.

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Section: Price Time Series Prediction Methodsmentioning

confidence: 99%

Dynamic optimization of open-pit coal mine production scheduling based on ARIMA and fuzzy structured element

Liu

Yang

et al. 2023

Front. Earth Sci.

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“…GDP (gross domestic product) is a representative index that reflects the final results of production activities in a given region during a given accounting period and measures the scale, speed, structure, and benefits of national economic development. It is also the primary index for determining economic development strategic objectives [1]. The purpose of GDP forecasting is to analyze and deal with previous year's GDP data, as well as to use certain tools and methods to predict future GDP trends, which will allow us to see clearly the economic growth trend in the process of national economic reform and development mode transformation and to make a more accurate choice for current work.…”

Section: Introductionmentioning

confidence: 99%

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

Hua

2022

Wireless Communications and Mobile Computing

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GDP (gross domestic product) is a key indicator for assessing a country’s or region’s macroeconomic situation, as well as a foundation for the government to develop economic development strategies and macroeconomic policies. Currently, the majority of methods for forecasting GDP are linear methods, which only take into account the linear factors that affect GDP. GDP (gross domestic product) is widely regarded as the most accurate indicator of a country’s economic health. GDP not only reflects a country’s economic development over time but can also reflect its national strength and wealth. As a result, the GDP trend forecast partially reflects China’s transformation and future development. The time series ARIMA (Autoregressive Integrated Moving Average) model and the BPNN (BP neural network) model are combined in this article to create the ARIMA-BPNN fusion prediction model. The predicted values of the two models were then weighted averaged to obtain the predicted values of the linear part of the improved fusion model. To get the predicted values of the improved fusion model, we weighted average the residual parts of the two models, predict the nonlinear residual with BPNN, and add the predicted values of the two parts. It is applied to the actual GDP forecast in H province from 2019 to 2022, and the actual forecast verifies the effectiveness of the fusion forecast model in the actual forecast.

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Coking coal futures price index forecasting with the neural network

Zhang

2022

Miner Econ

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Coal Price Forecast Based on ARIMA Model

Cited by 4 publications

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Dynamic optimization of open-pit coal mine production scheduling based on ARIMA and fuzzy structured element

Dynamic optimization of open-pit coal mine production scheduling based on ARIMA and fuzzy structured element

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

Coking coal futures price index forecasting with the neural network

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