PLS COMPUTATIONAL MODEL FOR PREDICTION IN TIME SERIES

Some data fluctuates rapidly in a short period of time. Classical and computational models are useful in predicting this<br>highly volatile data. In this study Partial Least Square Regression and computational neural network models are used to explore<br>stock market tendency. Thirteen variables are considered to predict the daily closing prices of BSE sensex data. To evaluate the<br>prediction ability of the models, standard error values are calculated. The results revealed that Nonparametric PLS regression<br>model is better in prediction.

部分数据会在短时间内发生剧烈波动。
经典模型与计算模型均可用于对这类高波动数据进行预测。
本研究采用偏最小二乘回归（Partial Least Square Regression）与计算神经网络模型，探究股票市场趋势。
研究选取13个变量，用于预测孟买证券交易所敏感指数（BSE Sensex）的每日收盘价。
为评估各模型的预测性能，研究人员计算了标准误差值。
结果表明，非参数偏最小二乘回归（Nonparametric PLS regression）模型具备更优异的预测表现。

搜集汇总

背景与挑战

背景概述

该数据集专注于时间序列预测，特别是针对高度波动的数据，如股市趋势。它使用偏最小二乘回归和计算神经网络模型，基于13个变量预测BSE sensex的每日收盘价，并通过标准误差评估，发现非参数PLS回归模型具有更好的预测能力。