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Time Series Forecasting using ARIMA Model: A Case Study of Mining Face Drilling Rig
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-5620-5265
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-1967-6604
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-7744-2155
2018 (English)In: ADVCOMP 2018: The Twelfth International Conference on Advanced Engineering Computing and Applications in Sciences / [ed] Claus-Peter Rückemann; Ahmad Rafi Qawasmeh, International Academy, Research and Industry Association (IARIA), 2018, p. 1-3Conference paper, Published paper (Refereed)
Abstract [en]

This study implements an Autoregressive Integrated Moving Average (ARIMA) model to forecast total cost of a face drilling rig used in the Swedish mining industry. The ARIMA model shows different forecasting abilities using different values of ARIMA parameters (p, d, q). However, better estimation for the ARIMA parameters is required for accurate forecasting. Artificial intelligence, such as multi objective genetic algorithm based on the ARIMA model, could provide other possibilities for estimating the parameters. Time series forecasting is widely used for production control, production planning, optimizing industrial processes and economic planning. Therefore, the forecasted total cost data of the face drilling rig can be used for life cycle cost analysis to estimate the optimal replacement time of this rig.

Place, publisher, year, edition, pages
International Academy, Research and Industry Association (IARIA), 2018. p. 1-3
Series
ADVCOMP, International Conference on Advanced Engineering Computing and Applications in Sciences, ISSN 2308-4499
Keywords [en]
ARIMA model, Data forecasting, Mining face drilling rig
National Category
Mineral and Mine Engineering Computer Sciences
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-71774ISI: 000464893300001OAI: oai:DiVA.org:ltu-71774DiVA, id: diva2:1266336
Conference
12th International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP 2018), Athens, Greece, November 18-22, 2018
Note

ISBN för värdpublikation: 978-1-61208-677-4

Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2021-08-23Bibliographically approved

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Type fulltextMimetype application/pdf

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Al-Chalabi, HussanAl-Douri, Yamur K.Lundberg, Jan

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