Data normalization techniques in swarm-based forecasting models for energy commodity spot price
Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study in...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
|
| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/13764/1/6.pdf |
| _version_ | 1825803245474283520 |
|---|---|
| author | Yusof, Yuhanis Mustaffa, Zuriani Kamaruddin, Siti Sakira |
| author_facet | Yusof, Yuhanis Mustaffa, Zuriani Kamaruddin, Siti Sakira |
| author_sort | Yusof, Yuhanis |
| collection | UUM |
| description | Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms. |
| first_indexed | 2024-07-04T05:53:43Z |
| format | Conference or Workshop Item |
| id | uum-13764 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:53:43Z |
| publishDate | 2014 |
| record_format | eprints |
| spelling | uum-137642016-05-26T06:47:20Z https://repo.uum.edu.my/id/eprint/13764/ Data normalization techniques in swarm-based forecasting models for energy commodity spot price Yusof, Yuhanis Mustaffa, Zuriani Kamaruddin, Siti Sakira QA76 Computer software Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms. 2014-12-04 Conference or Workshop Item NonPeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/13764/1/6.pdf Yusof, Yuhanis and Mustaffa, Zuriani and Kamaruddin, Siti Sakira (2014) Data normalization techniques in swarm-based forecasting models for energy commodity spot price. In: 3rd International Conference on Computer Engineering and Mathematical Sciences (ICCEMS 2014), 4-5 December 2014, Langkawi, Malaysia. (Unpublished) http://www.iccems.com/cms/ |
| spellingShingle | QA76 Computer software Yusof, Yuhanis Mustaffa, Zuriani Kamaruddin, Siti Sakira Data normalization techniques in swarm-based forecasting models for energy commodity spot price |
| title | Data normalization techniques in swarm-based forecasting models for energy commodity spot
price |
| title_full | Data normalization techniques in swarm-based forecasting models for energy commodity spot
price |
| title_fullStr | Data normalization techniques in swarm-based forecasting models for energy commodity spot
price |
| title_full_unstemmed | Data normalization techniques in swarm-based forecasting models for energy commodity spot
price |
| title_short | Data normalization techniques in swarm-based forecasting models for energy commodity spot
price |
| title_sort | data normalization techniques in swarm based forecasting models for energy commodity spot price |
| topic | QA76 Computer software |
| url | https://repo.uum.edu.my/id/eprint/13764/1/6.pdf |
| work_keys_str_mv | AT yusofyuhanis datanormalizationtechniquesinswarmbasedforecastingmodelsforenergycommodityspotprice AT mustaffazuriani datanormalizationtechniquesinswarmbasedforecastingmodelsforenergycommodityspotprice AT kamaruddinsitisakira datanormalizationtechniquesinswarmbasedforecastingmodelsforenergycommodityspotprice |