This empirical research endeavor seeks to enhance the accuracy of forecasting time series data in the banking sector by utilizing data from the Amman Stock Exchange (ASE). The study relied on daily closed price index data, spanning from October 2014 to December 2022, encompassing a total of 2048 observations. To attain statistically significant results, the research employs various mathematical techniques, including the non-linear spectral model, the maximum overlapping discrete wavelet transform (MODWT) based on the Coiflet function (C6), and the autoregressive integrated moving average (ARIMA) model. Notably, the study’s findings encompass the comprehensive explanation of all past events within the specified time frame, alongside the introduction of a novel forecasting model that amalgamates the most effective MODWT function (C6) with a tailored ARIMA model. Furthermore, this research underscores the effectiveness of MODWT in decomposing stock market data, particularly in identifying significant events characterized by high volatility, which thereby enhances forecasting accuracy. These results hold valuable implications for researchers and scientists across various domains, with a particular relevance to the fields of business and health sciences. The performance evaluation of the forecasting methodology is based on several mathematical criteria, including the mean absolute percentage error (MAPE), the mean absolute scaled error (MASE), and the root mean squared error (RMSE).

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通过银行时间序列分析提高预测准确性：安曼证券交易所案例研究

这项实证研究旨在利用安曼证券交易所 (ASE) 的数据来提高银行业时间序列数据预测的准确性。该研究依赖于 2014 年 10 月至 2022 年 12 月的每日收盘价格指数数据，总共包含 2048 个观察值。为了获得统计上显着的结果，该研究采用了各种数学技术，包括非线性谱模型、基于 Coiflet 函数 (C6) 的最大重叠离散小波变换 (MODWT) 以及自回归积分移动平均 (ARIMA) 模型。值得注意的是，该研究的结果包括对指定时间范围内所有过去事件的综合解释，同时引入了一种新颖的预测模型，该模型将最有效的 MODWT 函数 (C6) 与定制的 ARIMA 模型合并在一起。此外，这项研究强调了 MODWT 在分解股市数据方面的有效性，特别是在识别以高波动性为特征的重大事件方面，从而提高了预测准确性。这些结果对各个领域的研究人员和科学家具有重要意义，特别是与商业和健康科学领域相关。预测方法的性能评估基于多个数学标准，包括平均绝对百分比误差 (MAPE)、平均绝对比例误差 (MASE) 和均方根误差 (RMSE)。