Commodity price prediction using neural networks

Commodities and its prices play a large role in the economic policies of countries whether they are exporters or importer. The ability to forecast commodity prices is then an important factor in decision making. The neural network is theorised to be able to trend non-linear and non-stationary time-s...

Full description

Saved in:

Bibliographic Details
Main Author:	Chen, Tianyou
Other Authors:	Wang Lipo
Format:	Final Year Project
Language:	English
Published:	2019
Subjects:	DRNTU::Engineering::Electrical and electronic engineering
Online Access:	http://hdl.handle.net/10356/78064
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-78064
record_format	dspace
spelling	sg-ntu-dr.10356-780642023-07-07T16:30:55Z Commodity price prediction using neural networks Chen, Tianyou Wang Lipo School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Commodities and its prices play a large role in the economic policies of countries whether they are exporters or importer. The ability to forecast commodity prices is then an important factor in decision making. The neural network is theorised to be able to trend non-linear and non-stationary time-series data. Hence, this paper will evaluate the use of novel neural networks proposed by other researchers. The primary neural network examined are the recurrent neural networks. The use of EEMD (Ensemble Empirical Mode Decomposition) in neural networks were found to be generally positive. This paper then proposes using deeper networks architectures to further improve the use of EEMD in neural networks. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-11T07:47:00Z 2019-06-11T07:47:00Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78064 en Nanyang Technological University 40 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering Chen, Tianyou Commodity price prediction using neural networks
description	Commodities and its prices play a large role in the economic policies of countries whether they are exporters or importer. The ability to forecast commodity prices is then an important factor in decision making. The neural network is theorised to be able to trend non-linear and non-stationary time-series data. Hence, this paper will evaluate the use of novel neural networks proposed by other researchers. The primary neural network examined are the recurrent neural networks. The use of EEMD (Ensemble Empirical Mode Decomposition) in neural networks were found to be generally positive. This paper then proposes using deeper networks architectures to further improve the use of EEMD in neural networks.
author2	Wang Lipo
author_facet	Wang Lipo Chen, Tianyou
format	Final Year Project
author	Chen, Tianyou
author_sort	Chen, Tianyou
title	Commodity price prediction using neural networks
title_short	Commodity price prediction using neural networks
title_full	Commodity price prediction using neural networks
title_fullStr	Commodity price prediction using neural networks
title_full_unstemmed	Commodity price prediction using neural networks
title_sort	commodity price prediction using neural networks
publishDate	2019
url	http://hdl.handle.net/10356/78064
_version_	1772827917661765632

Commodity price prediction using neural networks

Similar Items