Climate change is an event that has significant effects as direct or indirect on ecosystem and living things. In order to be prepared for the effect of climate change, it is necessary to anticipate these changes and take measures for this change. Therefore, many studies have been carried out on changes in climate parameters in recent years. The most common method used in these studies is trend methods. Innovative Polygon Trend Analysis (IPTA) and Trend Polygon Star Concept are trend analysis methods. IPTA Method divides data series into two as first and second data set and analyzes these two data sets by comparing them with each other. Trend Polygon Star Concept analyzes distance between two months in data set in graph, which is result of IPTA, and shows analysis result by dividing it into four regions. Therefore, in this study, monthly average temperature data are analyzed by using this two-polygon method. This data set is for 22 years (1996–2017). Polygon graphics were created as a result of study. Besides, trend slopes and lengths of temperature data with IPTA Method were calculated. The values of graphs created with Trend Polygon Star Concept Method on x- and y-axis were given in a table. When the results of both analysis methods were examined for a station, the following results were observed. For example, a regular polygon was not seen in arithmetic mean and standard deviation graphs of IPTA Method of Bandirma Station. Besides, when general evaluation of arithmetic mean analysis results was examined an increasing trend in most months. When arithmetic average graph created by Trend Polygon Star Concept Method of Bandirma Station was examined, transition between two months was seen first and third region. When standard deviation graph was examined, transitions between two months were seen in all four regions.

气候变化是一种对生态系统和生物具有直接或间接影响的事件。为了为气候变化的影响做好准备，有必要预测这些变化并针对这种变化采取措施。因此，近年来对气候参数的变化进行了许多研究。这些研究中最常用的方法是趋势法。创新多边形趋势分析（IPTA）和趋势多边形星概念是趋势分析方法。IPTA 方法将数据系列分为两个作为第一和第二数据集，并通过相互比较来分析这两个数据集。Trend Polygon Star Concept 分析图表中数据集中两个月之间的距离，这是IPTA 的结果，并通过将其分为四个区域来显示分析结果。因此，在本研究中，月平均气温数据采用这种二多边形法进行分析。这个数据集是 22 年（1996-2017）。作为研究的结果创建了多边形图形。此外，使用IPTA方法计算了温度数据的趋势斜率和长度。表格中给出了在 x 和 y 轴上使用趋势多边形星概念方法创建的图形的值。当对一个站点检查两种分析方法的结果时，观察到以下结果。例如，在 Bandirma Station 的 IPTA 方法的算术平均值和标准偏差图中没有看到正多边形。此外，从算术平均分析结果的总体评价来看，大多数月份都有增加的趋势。当检查由 Bandirma Station 的 Trend Polygon Star Concept Method 创建的算术平均图时，两个月之间的过渡出现在第一和第三区域。当检查标准偏差图时，在所有四个区域都可以看到两个月之间的转变。