Mining time series data : case of predicting consumption patterns in steel industry

Fazel, A, Saraee, MH ORCID: https://orcid.org/0000-0002-3283-1912 and Shamsinejad, P 2010, Mining time series data : case of predicting consumption patterns in steel industry , in: The 2nd International Conference on Software Engineering and Data Mining, 23-25 June 2010, Chengdu, China.

PDF - Published Version Restricted to Repository staff only Download (462kB) | Request a copy

Abstract

Analyzing and predicting with Time series is a method which used in different fields, including consumption pattern analyzing and predicting. In this paper, required amount of inventory items have been predicted with time series. At first, desired data mining process is designed and implemented using Clementine data mining tool. We evaluate this process using the dataset from Iran's ZoabAhan steel company. Results show that by using this process not only we can model consumption patterns for the present time but also we can predict required stock items for future with adequate accuracy.

Item Type:	Conference or Workshop Item (Paper)
Schools:	Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title:	The 2nd International Conference on Software Engineering and Data Mining
Publisher:	IEEE
Related URLs:	Publication
Depositing User:	Prof. Mo Saraee
Date Deposited:	14 Jul 2017 08:29
Last Modified:	05 Sep 2018 21:24
URI:	http://usir.salford.ac.uk/id/eprint/43006

Actions (login required)

Edit record (repository staff only)