Consumer product prediction using machine learning

被引:0
|
作者
Ajitha, P. [1 ]
Tamilvizhi, T. [2 ]
Sowjanya, K. Naga [3 ]
Surendran, R. [4 ]
Bala, Bhoomeshwar [5 ]
机构
[1] Sathyabama Inst Sci & Technol, Sch Comp, Chennai, Tamil Nadu, India
[2] Panimalar Engn Coll, Dept Comp Sci & Engn, Chennai, Tamil Nadu, India
[3] Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala E, Dept Informat Technol, Chennai, Tamil Nadu, India
[4] Saveetha Inst Med & Tech Sci, Saveetha Sch Engn, Dept Comp Sci & Engn, Chennai, Tamil Nadu, India
[5] Debre Tabor Univ, Dept Comp Sci, Debra Tabor, Ethiopia
来源
关键词
Machine learning; Prediction; Neural network; Backpropagation; Support vector machine;
D O I
10.47974/JIOS-1415
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
Time-series forecasting is an approach that uses historical and current data to project future values over time or at a given point in time, while forecasting and prediction are often synonymous, there is one interesting detail. In some professions, forecasting may refer to data at a specific future point in time, whereas prediction refers to future data in general. Most widely used to determine the nature of stock prices. A series of analyses and modeling by a finance committee is to guide investors, professors of legal sciences, and processes. And that is why he proposes that this series argument not include a sliding window; they were wise to back then, and they gave up everything, anticipating stock values relative to her. The system presents the (GUI) Graphical User Interface as a stand-alone application. The proposed findings demonstrate a highly predicted accurate approach for nonlinear time series models that are difficult to obtain from traditional models.
引用
收藏
页码:565 / 574
页数:10
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