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

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.

为对比预测精度，本研究采用人工神经网络（Artificial Neural Networks）、差分整合移动平均自回归模型（Autoregressive Integrated Moving Average）与回归模型，基于训练数据集完成拟合后，利用测试集开展价格预测任务。本次研究共计算了三类精度评价指标：均方根误差（Root Mean Square Error）、平均绝对误差（Mean Absolute Error）以及平均绝对百分比误差（Mean Absolute Percentage Error）。为探明精度随样本量的变化规律，研究针对巴勒斯坦市场的日度、月度及季度股票收盘价时序数据，对上述模型展开了对比分析。