Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study

Safi Samir K; White Alexander K

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Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study

机译：使用人工神经网络对巴勒斯坦股票价格进行短期和长期预测：一项比较研究

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To compare the forecast accuracy, Artificial Neural Networks, Autoregressive Integrated Moving Average and regression models were fit with training data sets and then used to forecast prices in a test set. Three different measures of accuracy were computed: Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. To determine how the accuracy depends on sample size, models were compared between daily, monthly and quarterly time series of stock closing prices from Palestine.

机译：为了比较预测准确性，将人工神经网络，自回归综合移动平均值和回归模型与训练数据集进行拟合，然后将其用于预测测试集中的价格。计算了三种不同的精度度量：均方根误差，均值绝对误差和均值绝对百分比误差。为了确定准确性如何取决于样本数量，对巴勒斯坦的股票收盘价的每日，每月和季度时间序列之间的模型进行了比较。

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《Electronic Journal of Applied Statistical Analysis》 |2017年第1期|共15页
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Safi Samir K; White Alexander K;
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中图分类统计学;
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Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study

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