Intelligent time series fast forecasting for fashion sales: A research agenda

Tsan Ming Choi, Chi Leung Hui, Yong Yu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

16 Citations (Scopus)

Abstract

Forecasting for the time series sales data of fashion products is crucial for many fashion companies. However, both the traditional statistical methods and the more advanced intelligent artificial intelligence (AI) methods suffer serious drawbacks in which the former's performance depend highly on the time series data's features whereas the latter ones are slow. There is hence a need to call for the development of an intelligent time series forecasting system which is fast, versatile and can achieve a reasonably high accuracy. In this paper, we explore this issue and propose a research agenda for future studies around intelligent fast forecasting system for the prediction of fashion sales time series.
Original languageEnglish
Title of host publicationProceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages1010-1014
Number of pages5
Volume3
DOIs
Publication statusPublished - 7 Nov 2011
Event2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China
Duration: 10 Jul 201113 Jul 2011

Conference

Conference2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Country/TerritoryChina
CityGuilin, Guangxi
Period10/07/1113/07/11

Keywords

  • fashion
  • fast forecasting
  • Intelligent forecasting
  • research agenda
  • sales forecasting

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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