Di Martino Ferdinando, Sessa Salvatore
Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy.
Centro di Ricerca Interdipartimentale di Ricerca A. Calza Bini, Università degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy.
Sensors (Basel). 2019 Aug 19;19(16):3611. doi: 10.3390/s19163611.
We present a new seasonal forecasting method based on F-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series' trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F-transform components for each seasonal subset are calculated as well. The inverse F-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index datasets obtained from daily weather data measured from weather stations of the Campania Region (Italy) during the months of July and August from 2003 to 2017. We compare the results obtained with the statistics Autoregressive Integrated Moving Average (ARIMA), Automatic Design of Artificial Neural Networks (ADANN), and the seasonal F-transform methods, showing that the best results are just given by our approach.
我们提出了一种基于F变换（一阶模糊变换）的新季节预测方法，并将其应用于天气数据集。本研究的目的是提高应用于季节时间序列的基于模糊变换的预测方法的性能。通过多项式拟合获得时间序列的趋势；然后，将数据集划分为S个季节子集，并计算每个季节子集的直接F变换分量。逆F变换用于预测未来天气参数的值。我们在2003年至2017年7月和8月期间从意大利坎帕尼亚地区气象站测量的每日天气数据获得的热指数数据集上测试了我们的方法。我们将所得结果与统计自回归积分移动平均法（ARIMA）、人工神经网络自动设计法（ADANN）以及季节F变换方法进行比较，结果表明我们的方法给出了最佳结果。
