Abstract
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.
Highlights
Today, seasonal time series forecasting represents a crucial activity in many fields such as macroeconomics, finance and marketing, and weather and climate analysis
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
We propose a novel seasonal time series forecasting algorithm based on the direct and inverse
Summary
Seasonal time series forecasting represents a crucial activity in many fields such as macroeconomics, finance and marketing, and weather and climate analysis. One of the processes for the evolution of the climate of an area of study is to analyze continuously measured data from weather stations and to capture and monitor changes in seasonal values of climate parameters. In this analysis, a significant role is played by seasonal time series forecasting algorithms applied to weather data. Time series forecasting techniques are applied to time-measured data in order to predict future trends of a variable. A characteristic detectable in many time series is seasonality, consisting in a regularly repeating pattern of highs and lows related to specific time periods such as seasons, months, weeks, and so on
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