Loading…
Sales Forecasting Using ANNs or RNNs - A Case Study of Freeway Service Station in Taiwan
Following recent progress in neural networks, an increasing number of researchers have applied this technique to sales forecasting. The accurate prediction of sales enables businesses to reduce stockpiles and scrap costs. However, it has been heavily debated whether artificial neural networks (ANNs)...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| Online Access: | Request full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | 2 |
| container_issue | |
| container_start_page | 1 |
| container_title | |
| container_volume | |
| creator | Lin, Yi-Fu Cheng, Chia-Sheng Chen, Yi-Chung |
| description | Following recent progress in neural networks, an increasing number of researchers have applied this technique to sales forecasting. The accurate prediction of sales enables businesses to reduce stockpiles and scrap costs. However, it has been heavily debated whether artificial neural networks (ANNs) or recurrent neural networks (RNNs) are the most appropriate for sales forecasting. A number of factors influence sales, and it is difficult to determine whether daily business conditions are independent of each other. To fill this gap in the literature, we first employed conventional data analysis to identify suitable input fields for ANN and RNN. We then input these fields into both types of neural network. To verify the validity of our discussion, we conducted an analysis using a real-world sales dataset from a service station on a freeway in Taiwan. |
| doi_str_mv | 10.1109/ICCE-TW52618.2021.9603197 |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9603197</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9603197</ieee_id><sourcerecordid>9603197</sourcerecordid><originalsourceid>FETCH-LOGICAL-i485-a08d2202d1496400cc8c61a6ae8cab9713e478004d89b60e44ec0c4ba60f9aa43</originalsourceid><addsrcrecordid>eNotkEFLw0AUhFdBsNb-Ai_rD0h8u9lsdo8hNLZQKtiI3srL5kVWaiLZasm_12Av8x1mGIZh7F5ALATYh3VRLKPqNZVamFiCFLHVkAibXbAboXWqkkQauGQzmWZpZKRR12wRwgfAlAIQdsbednigwMt-IIfh6Lt3_hImzbfbwPuBP0-MeM4LDMR3x-9m5H3Ly4HohCPf0fDj3WTg0fcd9x2v0J-wu2VXLR4CLc6cs6pcVsUq2jw9rot8E3ll0gjBNPJveiOU1QrAOeO0QI1kHNY2EwmpzACoxthaAylFDpyqUUNrEVUyZ3f_tZ6I9l-D_8Rh3J9_SH4BVyBRRQ</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Sales Forecasting Using ANNs or RNNs - A Case Study of Freeway Service Station in Taiwan</title><source>IEEE Xplore All Conference Series</source><creator>Lin, Yi-Fu ; Cheng, Chia-Sheng ; Chen, Yi-Chung</creator><creatorcontrib>Lin, Yi-Fu ; Cheng, Chia-Sheng ; Chen, Yi-Chung</creatorcontrib><description>Following recent progress in neural networks, an increasing number of researchers have applied this technique to sales forecasting. The accurate prediction of sales enables businesses to reduce stockpiles and scrap costs. However, it has been heavily debated whether artificial neural networks (ANNs) or recurrent neural networks (RNNs) are the most appropriate for sales forecasting. A number of factors influence sales, and it is difficult to determine whether daily business conditions are independent of each other. To fill this gap in the literature, we first employed conventional data analysis to identify suitable input fields for ANN and RNN. We then input these fields into both types of neural network. To verify the validity of our discussion, we conducted an analysis using a real-world sales dataset from a service station on a freeway in Taiwan.</description><identifier>EISSN: 2575-8284</identifier><identifier>EISBN: 1665433280</identifier><identifier>EISBN: 9781665433280</identifier><identifier>DOI: 10.1109/ICCE-TW52618.2021.9603197</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Conferences ; Costs ; Data analysis ; Forecasting ; Recurrent neural networks ; Traffic control</subject><ispartof>2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2021, p.1-2</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9603197$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9603197$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lin, Yi-Fu</creatorcontrib><creatorcontrib>Cheng, Chia-Sheng</creatorcontrib><creatorcontrib>Chen, Yi-Chung</creatorcontrib><title>Sales Forecasting Using ANNs or RNNs - A Case Study of Freeway Service Station in Taiwan</title><title>2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)</title><addtitle>ICCE-TW</addtitle><description>Following recent progress in neural networks, an increasing number of researchers have applied this technique to sales forecasting. The accurate prediction of sales enables businesses to reduce stockpiles and scrap costs. However, it has been heavily debated whether artificial neural networks (ANNs) or recurrent neural networks (RNNs) are the most appropriate for sales forecasting. A number of factors influence sales, and it is difficult to determine whether daily business conditions are independent of each other. To fill this gap in the literature, we first employed conventional data analysis to identify suitable input fields for ANN and RNN. We then input these fields into both types of neural network. To verify the validity of our discussion, we conducted an analysis using a real-world sales dataset from a service station on a freeway in Taiwan.</description><subject>Artificial neural networks</subject><subject>Conferences</subject><subject>Costs</subject><subject>Data analysis</subject><subject>Forecasting</subject><subject>Recurrent neural networks</subject><subject>Traffic control</subject><issn>2575-8284</issn><isbn>1665433280</isbn><isbn>9781665433280</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2021</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkEFLw0AUhFdBsNb-Ai_rD0h8u9lsdo8hNLZQKtiI3srL5kVWaiLZasm_12Av8x1mGIZh7F5ALATYh3VRLKPqNZVamFiCFLHVkAibXbAboXWqkkQauGQzmWZpZKRR12wRwgfAlAIQdsbednigwMt-IIfh6Lt3_hImzbfbwPuBP0-MeM4LDMR3x-9m5H3Ly4HohCPf0fDj3WTg0fcd9x2v0J-wu2VXLR4CLc6cs6pcVsUq2jw9rot8E3ll0gjBNPJveiOU1QrAOeO0QI1kHNY2EwmpzACoxthaAylFDpyqUUNrEVUyZ3f_tZ6I9l-D_8Rh3J9_SH4BVyBRRQ</recordid><startdate>20210915</startdate><enddate>20210915</enddate><creator>Lin, Yi-Fu</creator><creator>Cheng, Chia-Sheng</creator><creator>Chen, Yi-Chung</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20210915</creationdate><title>Sales Forecasting Using ANNs or RNNs - A Case Study of Freeway Service Station in Taiwan</title><author>Lin, Yi-Fu ; Cheng, Chia-Sheng ; Chen, Yi-Chung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i485-a08d2202d1496400cc8c61a6ae8cab9713e478004d89b60e44ec0c4ba60f9aa43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Conferences</topic><topic>Costs</topic><topic>Data analysis</topic><topic>Forecasting</topic><topic>Recurrent neural networks</topic><topic>Traffic control</topic><toplevel>online_resources</toplevel><creatorcontrib>Lin, Yi-Fu</creatorcontrib><creatorcontrib>Cheng, Chia-Sheng</creatorcontrib><creatorcontrib>Chen, Yi-Chung</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lin, Yi-Fu</au><au>Cheng, Chia-Sheng</au><au>Chen, Yi-Chung</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Sales Forecasting Using ANNs or RNNs - A Case Study of Freeway Service Station in Taiwan</atitle><btitle>2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)</btitle><stitle>ICCE-TW</stitle><date>2021-09-15</date><risdate>2021</risdate><spage>1</spage><epage>2</epage><pages>1-2</pages><eissn>2575-8284</eissn><eisbn>1665433280</eisbn><eisbn>9781665433280</eisbn><abstract>Following recent progress in neural networks, an increasing number of researchers have applied this technique to sales forecasting. The accurate prediction of sales enables businesses to reduce stockpiles and scrap costs. However, it has been heavily debated whether artificial neural networks (ANNs) or recurrent neural networks (RNNs) are the most appropriate for sales forecasting. A number of factors influence sales, and it is difficult to determine whether daily business conditions are independent of each other. To fill this gap in the literature, we first employed conventional data analysis to identify suitable input fields for ANN and RNN. We then input these fields into both types of neural network. To verify the validity of our discussion, we conducted an analysis using a real-world sales dataset from a service station on a freeway in Taiwan.</abstract><pub>IEEE</pub><doi>10.1109/ICCE-TW52618.2021.9603197</doi><tpages>2</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2575-8284 |
| ispartof | 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2021, p.1-2 |
| issn | 2575-8284 |
| language | eng |
| recordid | cdi_ieee_primary_9603197 |
| source | IEEE Xplore All Conference Series |
| subjects | Artificial neural networks Conferences Costs Data analysis Forecasting Recurrent neural networks Traffic control |
| title | Sales Forecasting Using ANNs or RNNs - A Case Study of Freeway Service Station in Taiwan |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T19%3A45%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Sales%20Forecasting%20Using%20ANNs%20or%20RNNs%20-%20A%20Case%20Study%20of%20Freeway%20Service%20Station%20in%20Taiwan&rft.btitle=2021%20IEEE%20International%20Conference%20on%20Consumer%20Electronics-Taiwan%20(ICCE-TW)&rft.au=Lin,%20Yi-Fu&rft.date=2021-09-15&rft.spage=1&rft.epage=2&rft.pages=1-2&rft.eissn=2575-8284&rft_id=info:doi/10.1109/ICCE-TW52618.2021.9603197&rft.eisbn=1665433280&rft.eisbn_list=9781665433280&rft_dat=%3Cieee_CHZPO%3E9603197%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i485-a08d2202d1496400cc8c61a6ae8cab9713e478004d89b60e44ec0c4ba60f9aa43%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9603197&rfr_iscdi=true |