SHRINKING THE UNCERTAINTY IN ONLINE SALES PREDICTION WITH TIME SERIES ANALYSIS
In any production environment, processing is centered on the manufacture of products. It is important to get adequate volumes of orders for those products. However, merely getting orders is not enough for the long-term sustainability of multinationals. They need to know the demand for their products...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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ICT Academy of Tamil Nadu
2014-10-01
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| Series: | ICTACT Journal on Soft Computing |
| Subjects: | |
| Online Access: | http://ictactjournals.in/paper/IJSC_Splissue_Oct2014_Paper_6_869_874.pdf |
| Summary: | In any production environment, processing is centered on the manufacture of products. It is important to get adequate volumes of orders for those products. However, merely getting orders is not enough for the long-term sustainability of multinationals. They need to know the demand for their products well in advance in order to compete and win in a highly competitive market. To assess the demand of a product we need to track its order behavior and predict the future response of customers depending on the present dataset as well as historical dataset. In this paper we propose a systematic, time-series based scheme to perform this task using the Hadoop framework and Holt-Winter prediction function in the R environment to show the sales forecast for forthcoming years. |
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| ISSN: | 0976-6561 2229-6956 |