東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Using artificial neural networks to ...

Khazem, Hassan A.

FindBook

Google Book

Amazon

博客來

Using artificial neural networks to forecast the futures prices of crude oil.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Using artificial neural networks to forecast the futures prices of crude oil./
作者:	Khazem, Hassan A.
面頁冊數:	153 p.
附註:	Adviser: A. K. Mazouz.
Contained By:	Dissertation Abstracts International69-01A.
標題:	Artificial Intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3295968
ISBN:	9780549413936

Using artificial neural networks to forecast the futures prices of crude oil.
Khazem, Hassan A.

Using artificial neural networks to forecast the futures prices of crude oil. - 153 p.

Adviser: A. K. Mazouz.

Thesis (D.B.A.)--Nova Southeastern University, 2008.

Crude oil is the commodity de jour and its pricing is of paramount importance to the layperson as well as to any responsible government. However, one of the main challenges facing econometric pricing models is the forecasting accuracy. Historically, linear and non-linear time series models were used. Although, a great success was achieved in that regard, yet there were no definite and universal conclusions drawn. The crude oil forecasting field is still wide open for improvement, especially when applying different forecasting models and alternative techniques. Toward this end, the proposed research implemented Artificial Neural Network models (ANN). The models will forecast the daily crude oil futures prices from 1996 to 2006, listed in NYMEX. Due to the nonlinearity presented by the test results of the crude oil pricing, it is expected that the ANN models will improve forecasting accuracy. An evaluation of the outcomes of the forecasts among different models was done to authenticate that this is undeniably the situation.

ISBN: 9780549413936Subjects--Topical Terms:

769149
Artificial Intelligence.

Using artificial neural networks to forecast the futures prices of crude oil.
LDR:01983nam 2200313 a 45 001 958993
005 20110704
008 110704s2008 ||||||||||||||||| ||eng d
020 $a 9780549413936
035 $a (UMI)AAI3295968
035 $a AAI3295968
040 $a UMI $c UMI
100 1 $a Khazem, Hassan A. $3 1282461
245 1 0 $a Using artificial neural networks to forecast the futures prices of crude oil.
300 $a 153 p.
500 $a Adviser: A. K. Mazouz.
500 $a Source: Dissertation Abstracts International, Volume: 69-01, Section: A, page: 0319.
502 $a Thesis (D.B.A.)--Nova Southeastern University, 2008.
520 $a Crude oil is the commodity de jour and its pricing is of paramount importance to the layperson as well as to any responsible government. However, one of the main challenges facing econometric pricing models is the forecasting accuracy. Historically, linear and non-linear time series models were used. Although, a great success was achieved in that regard, yet there were no definite and universal conclusions drawn. The crude oil forecasting field is still wide open for improvement, especially when applying different forecasting models and alternative techniques. Toward this end, the proposed research implemented Artificial Neural Network models (ANN). The models will forecast the daily crude oil futures prices from 1996 to 2006, listed in NYMEX. Due to the nonlinearity presented by the test results of the crude oil pricing, it is expected that the ANN models will improve forecasting accuracy. An evaluation of the outcomes of the forecasts among different models was done to authenticate that this is undeniably the situation.
590 $a School code: 1191.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Business Administration, Banking. $3 1018458
650 4 $a Economics, Finance. $3 626650
650 4 $a Energy. $3 876794
650 4 $a Engineering, System Science. $3 1018128
690 $a 0508
690 $a 0770
690 $a 0790
690 $a 0791
690 $a 0800
710 2 $a Nova Southeastern University. $3 1017571
773 0 $t Dissertation Abstracts International $g 69-01A.
790 $a 1191
790 1 0 $a Mazouz, A. K., $e advisor
791 $a D.B.A.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3295968