概述:利用不同技术对人类压力的时间序列数据进行建模和预测

Surindar Gopalrao Wawale , Aadarsh Bisht , Sonali Vyas , Chutimon Narawish , Samrat Ray
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引用次数: 3

摘要

外汇是一个重要的货币指标。该指数是一个国家发展的主要因素。本研究考察了货币交易对随机漫步版本、指数平滑模型、双指数平滑模型和冬冬天气模型的影响,并使用每个对称损失因子的准确性水平和不对称使用矩形(MSE)误差、平均总偏差(MAD)和平均总百分比误差(MAPE)来判断时尚预测的表现。从精度评级来看,双滑块版本的解释器可用于预测和平滑一系列具有三种不同版本的货币汇率。为了测试赤池信息标准(AIC)小额货币的几个模型,我们研究了包含常规变化(ARIMA)的自回归模型,该模型可用于预测南亚地方合作(SAARC)的资金变化。这项研究允许发现不同的策略,并减少最终导致健康的人类智力类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An overview: Modeling and forecasting of time series data using different techniques in reference to human stress

Forex is an important currency indicator. The index is a major factor in the development of the country. This look examines the effects of currency trading on the Random stroll version, Exponential Smoothing One, Double Exponential Smoothing and Holt-wintry weather models and the performance of the fashion forecast were judged using the accuracy level of each symmetric loss factor and asymmetric used rectangular (MSE) errors, mean Total Deviations (MAD) and mean Total percentage errors (MAPE). From a precision rating, a double slider version of the interpreter can be used to anticipate and smooth out a series of currency exchange rates with three different versions. In an effort to test several models of the Akaike information Criterion (AIC) small currency, we have examined the Autoregressive version that incorporates conventional change (ARIMA) that can be used to anticipate the change in funding for the South Asian Local Cooperation (SAARC). This research allows for the discovery of different strategies and reduces the type of human intelligence which ultimately leads to good health.

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来源期刊
Neuroscience informatics
Neuroscience informatics Surgery, Radiology and Imaging, Information Systems, Neurology, Artificial Intelligence, Computer Science Applications, Signal Processing, Critical Care and Intensive Care Medicine, Health Informatics, Clinical Neurology, Pathology and Medical Technology
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