Monthly Rainfall Forecasting Using High Order Singh’s Fuzzy Time Series Based on Interval Ratio Methods: Case Study Semarang City, Indonesia

Erikha Feriyanto, Farikhin, N. P. Puspita
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Abstract

Aims: Sample: To determine the effectiveness of the proposed forecasting method, namely Singh's fuzzy time series based on high order (third order) interval ratios. And find out the forecasting results in January 2022. Study Design:  Modification of Singh's fuzzy time series based on interval ratios. Place and Duration of Study: Sample: monthly rainfall data for Semarang City from January 2017 to December 2021. Methodology: The method proposed by the researcher is the Singh fuzzy time series forecasting method based on high order (third order) interval ratios. This research method uses a combination of Chen and Singh's fuzzy time series. Applying Chen's fuzzy time series in the section determining the universe of discourse () to fuzzification which includes determining the universe of discourse, determining interval partitions, forming Fuzzy Logical Relationships and Fuzzy Logical Relationship Groups. Then apply Singh's fuzzy time series to the forecasting part. Finally, calculate the Average Forecasting Error Rate (AFER) to test forecasting performance. In the forecasting part, it is obtained through a heuristic approach by building high order forecasting rules to obtain better results and have an effect on very small AFER values. In the step of determining the interval partition, this research uses the interval ratio method which aims to reflect variations in historical data. Conclusion: Based on the calculation of the AFER value, the AFER for third order is 0.2422%. It can be said that Singh's fuzzy time series forecasting method based on high order (third order) interval ratios on monthly rainfall data for Semarang City from January 2017 to December 2021 is very good. And the rainfall forecast for January 2022 is 196.80 mm3 or into the category of very heavy rain.
使用基于区间比方法的高阶辛格模糊时间序列进行月降雨量预报:印度尼西亚三宝垄市案例研究
目的:样本:确定所提出的预测方法(即基于高阶(三阶)区间比率的辛格模糊时间序列)的有效性。并找出 2022 年 1 月的预测结果。研究设计: 基于区间比率对辛格模糊时间序列进行修改。研究地点和时间:样本:三宝垄市 2017 年 1 月至 2021 年 12 月的月降雨量数据。研究方法:研究者提出的方法是基于高阶(三阶)区间比率的辛格模糊时间序列预测方法。该研究方法采用了陈氏和辛格模糊时间序列的组合。在确定话语范围()部分应用陈氏模糊时间序列进行模糊化,包括确定话语范围、确定区间分区、形成模糊逻辑关系和模糊逻辑关系组。然后将辛格模糊时间序列应用于预测部分。最后,计算平均预测误差率(AFER)以检验预测性能。在预测部分,通过启发式方法建立高阶预测规则,以获得更好的结果,并对极小的 AFER 值产生影响。在确定区间分区的步骤中,本研究采用了区间比率法,旨在反映历史数据的变化。结论根据 AFER 值的计算,三阶的 AFER 为 0.2422%。可以说,辛格基于高阶(三阶)区间比的模糊时间序列预测方法对三宝垄市 2017 年 1 月至 2021 年 12 月的月降雨量数据的预测效果非常好。而 2022 年 1 月的降雨量预测值为 196.80 立方毫米,属于特大暴雨。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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