使用机器学习进行消费产品预测

IF 1.1 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE
P. Ajitha, T. Tamilvizhi, K. Sowjanya, R. Surendran, B. Bala
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引用次数: 0

摘要

时间序列预测是一种使用历史和当前数据来预测一段时间内或给定时间点的未来值的方法,虽然预测和预测通常是同义词,但有一个有趣的细节。在某些专业中,预测可能是指未来某个特定时间点的数据,而预测则是指未来的一般数据。最广泛用于确定股票价格的性质。金融委员会的一系列分析和建模是为了指导投资者、法学教授和流程。这就是为什么他提出这个级数论证不包括滑动窗口;他们当时是明智的,他们放弃了一切,预测了股票相对于她的价值。该系统将图形用户界面(GUI)作为一个独立的应用程序。提出的研究结果表明,对于难以从传统模型中获得的非线性时间序列模型,该方法具有高度预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Consumer product prediction using machine learning
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.
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来源期刊
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES INFORMATION SCIENCE & LIBRARY SCIENCE-
自引率
21.40%
发文量
88
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