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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.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This study implements an AutoregressiveIntegrated Moving Average (ARIMA) model to forecast totalcost of a face drilling rig used in the Swedish mining industry.The ARIMA model shows different forecasting abilities usingdifferent values of ARIMA parameters (p, d, q). However,better estimation for the ARIMA parameters is required foraccurate forecasting. Artificial intelligence, such as multiobjective genetic algorithm based on the ARIMA model, couldprovide other possibilities for estimating the parameters. Timeseries forecasting is widely used for production control,production planning, optimizing industrial processes andeconomic planning. Therefore, the forecasted total cost data ofthe face drilling rig can be used for life cycle cost analysis toestimate the optimal replacement time of this rig.

Place, publisher, year, edition, pages
Athens, Greece: International Academy, Research and Industry Association (IARIA), 2018. p. 1-3, article id advcomp_2018_1_10_20007
Keywords [en]
ARIMA model, Data forecasting, Mining face drilling rig
National Category
Computer Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-71774ISBN: 978-1-61208-677-4 (electronic)OAI: oai:DiVA.org:ltu-71774DiVA, id: diva2:1266336
Conference
The Twelfth International Conference on Advanced Engineering Computing and Applications in Sciences
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-09-13

Open Access in DiVA

fulltext(316 kB)10 downloads
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File name FULLTEXT01.pdfFile size 316 kBChecksum SHA-512
7bf819e3c2a4bc617019faa9ccc7cb512ff2d3023a0bfb9f7789673ff34ac6d6eb51055710e25dab83ec77d9db32d7044ff678132c245a0e1e20aebe8068e984
Type fulltextMimetype application/pdf

Authority records BETA

Lundberg, Jan

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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