Recent Şen innovative trend analysis method (Şen_ITA) has high graphical visual ability in addition to quantitative trend identification analysis aspects. However, it is important to determine critical trend values as to whether they are significant according to a certain statistical significance level. In order to achieve such a goal, a certain amount or average percentages (5%, 10%, and 20%), bootstraps, and variance correction methods are frequently used by the researchers as evident from the literature. These methods accept either approximate critical values or require the transformation of the time series to suit their assumption requirements. In this paper, Monte Carlo simulation studies are used to measure error rates (type 1) as suggestion for critical trend value determinations for the Şen_ITA method. Şen_ITA critical trend formula has been developed originally depending on type 1 error rate exceedance expectation. The improved method (ITA_R) as suggested in this paper provides successful results by comparing it with the classic Mann-Kendall (MK) method. Furthermore, the ITA_R and MK methods have been applied to the mean daily maximum temperature series from England. The resulting trend values are generally consistent with the MK method and show an upward trend in all regions of England and during all seasons.

除了定量趋势识别分析方面，最新的创新趋势分析方法（en_ITA）还具有很高的图形视觉功能。但是，重要的是要根据某个统计显着性水平确定关键趋势值是否显着。为了实现这一目标，研究人员经常使用一定数量或平均百分比（5％，10％和20％），引导程序和方差校正方法，这从文献中可以明显看出。这些方法要么接受近似临界值，要么需要转换时间序列以符合其假设要求。在本文中，蒙特卡罗模拟研究用于测量错误率（类型1），作为Şen_ITA方法的关键趋势值确定的建议。Şen_ITA临界趋势公式最初是根据1类错误率超出预期而开发的。通过与经典的Mann-Kendall（MK）方法进行比较，本文提出的改进方法（ITA_R）提供了成功的结果。此外，ITA_R和MK方法已应用于英格兰的日平均最高气温序列。所得趋势值通常与MK方法一致，并且在英格兰所有地区和所有季节中都显示出上升趋势。