AGRICULTURAL COMMODITY PRICE PREDICTION USING DEEP LEARNING TECHNIQUES: THE CASE OF ETHIOPIAN COMMODITY EXCHANGE (ECX

dc.contributor.author	DERBABAW, AGERSELAM
dc.date.accessioned	2024-12-30T07:32:35Z
dc.date.available	2024-12-30T07:32:35Z
dc.date.issued	2024-12-30
dc.identifier.uri	http://hdl.handle.net/123456789/8116
dc.description.abstract	The experimental results showed that the gated recurrent units (GRU) working with the selected features, gotten prediction error rate of 2.54 , remarkably good compared to Long short term memory(LSTM) based on ECX dataset, with a prediction error rate of 2.73.	en_US
dc.description.sponsorship	uog	en_US
dc.language.iso	en	en_US
dc.subject	The experimental results showed that the gated recurrent units (GRU) working with the selected features, gotten prediction error rate of 2.54 , remarkably good compared to Long short term memory(LSTM) based on ECX dataset, with a prediction error rate of 2.73.	en_US
dc.title	AGRICULTURAL COMMODITY PRICE PREDICTION USING DEEP LEARNING TECHNIQUES: THE CASE OF ETHIOPIAN COMMODITY EXCHANGE (ECX	en_US
dc.type	Thesis	en_US