Article
Version 1
Preserved in Portico This version is not peer-reviewed
Time Series Seasonal Analysis Based on Fuzzy Transforms
Version 1
: Received: 9 October 2017 / Approved: 9 October 2017 / Online: 9 October 2017 (16:58:43 CEST)
A peer-reviewed article of this Preprint also exists.
Di Martino, F.; Sessa, S. Time Series Seasonal Analysis Based on Fuzzy Transforms. Symmetry 2017, 9, 281. Di Martino, F.; Sessa, S. Time Series Seasonal Analysis Based on Fuzzy Transforms. Symmetry 2017, 9, 281.
Abstract
We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of a an assigned output. Like first example, we use the daily weather dataset of the municipality of Naples (Italy) starting from data collected from 2003 till to 2015 making predictions on the following outputs: mean temperature, max temperature and min temperature, all considered daily. Like second example, we use the daily mean temperature measured at the weather station “Chiavari Caperana” in the Liguria Italian Region. We compare the results with our method, the average seasonal variation, ARIMA and the usual fuzzy transforms concluding that the best results are obtained under our approach in both examples.
Keywords
ARIMA; forecasting; fuzzy partition; fuzzy transform; time series
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
