Seasonal Time Series Forecasting by F1-Fuzzy Transform

We present a new seasonal forecasting method based on F1-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 obtaine...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-08, Vol.19 (16), p.3611
Main Authors: Di Martino, Ferdinando, Sessa, Salvatore
Format: Article
Language:English
Subjects:
Autoregressive models
Climate change
Datasets
F-transform
F1-transform forecasting
Forecasting
Forecasting techniques
Fuzzy sets
Genetic algorithms
Heat
Meteorological data
Methods
Neural networks
Polynomials
seasonal time series
Statistical analysis
Test procedures
Time series
Trends
TSSF
TSSF1
Weather forecasting
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Summary:We present a new seasonal forecasting method based on F1-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 F1-transform components for each seasonal subset are calculated as well. The inverse F1-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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19163611