NDLI: Estimating multivariate conditional distributions via neural networks and global optimization

Content Provider	IEEE Xplore Digital Library
Author	Stutzle, E.A. Hrycej, T.
Copyright Year	2002
Description	Author affiliation: Res. Center, DaimlerChrysler AG, Ulm, Germany (Stutzle, E.A.; Hrycej, T.)
Abstract	A new concept for modelling and forecasting is introduced. The maximum likelihood principle is used to identify the multivariate distribution of forecast variables, conditional on given attributes of forecast context. The distribution parameters are conditional on input features such as properties of the product. The conditional distribution parameters are estimated by a global optimization method, using neural networks for functional approximation. The goal is to construct a general attribute-based forecast model, which can be applied to novel cases with new attribute combinations. The information about a complete distribution of forecasts can be used to quantify the reliability of the forecast. The reliability information is particularly useful for decision support, e.g. if the forecast error causes strongly asymmetric costs. This is illustrated on a case study concerning the spare parts demand forecast.
Sponsorship	IEEE IEEE Neural Networks Soc. (NNS) Int. Neural Network Soc
Starting Page	1767
Ending Page	1772
File Size	586845
Page Count	6
File Format	PDF
ISBN	0780372786
ISSN	10987576
DOI	10.1109/IJCNN.2002.1007786
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Demand forecasting Predictive models Covariance matrix Decision making Data mining Casting Maximum likelihood estimation Parameter estimation Optimization methods
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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