TY - GEN
T1 - Cloud-based evolving intelligent method for weather time series prediction
AU - Soares, Eduardo
AU - Mota, Vania
AU - Poucas, Ricardo
AU - Leite, Daniel
N1 - Funding Information:
Eduardo Soares, Vania Mota, Ricardo Poucas, and Daniel Leite are with the Graduate Program in Systems and Automation Engineering, Federal University of Lavras, 37200-000 BRA, e-mail: edu.soares999@gmail.com; vani-amota33@gmail.com; ricardo.poucas@gmail.com; daniel.leite@deg.ufla.br. This work was financially supported by CNPq, the Brazilian National Council for Scientific and Technological Development.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/23
Y1 - 2017/8/23
N2 - This paper concerns the application of a cloud-based intelligent evolving method, namely, a typicality-and-eccentricity-based method for data analysis (TEDA), to predict monthly mean temperature in different cities of Brazil. Past values of maximum, minimum and mean monthly temperature, as well as previous values of exogenous variables such as cloudiness, rainfall and humidity were considered in the analysis. A non-parametric Spearman correlation based method is proposed to rank and select the most relevant features and time delays for a more accurate prediction. The datasets were obtained from weather stations located in main cities such as Sao Paulo, Manaus, and Porto Alegre. These cities are known to have particular weather characteristics. TEDA prediction results are compared with results provided by the evolving Takagi-Sugeno (eTS) and the extended Takagi-Sugeno (xTS) methods. In general, TEDA provided slightly more accurate predictions at the price of a higher computational cost.
AB - This paper concerns the application of a cloud-based intelligent evolving method, namely, a typicality-and-eccentricity-based method for data analysis (TEDA), to predict monthly mean temperature in different cities of Brazil. Past values of maximum, minimum and mean monthly temperature, as well as previous values of exogenous variables such as cloudiness, rainfall and humidity were considered in the analysis. A non-parametric Spearman correlation based method is proposed to rank and select the most relevant features and time delays for a more accurate prediction. The datasets were obtained from weather stations located in main cities such as Sao Paulo, Manaus, and Porto Alegre. These cities are known to have particular weather characteristics. TEDA prediction results are compared with results provided by the evolving Takagi-Sugeno (eTS) and the extended Takagi-Sugeno (xTS) methods. In general, TEDA provided slightly more accurate predictions at the price of a higher computational cost.
UR - http://www.scopus.com/inward/record.url?scp=85030181792&partnerID=8YFLogxK
U2 - 10.1109/FUZZ-IEEE.2017.8015532
DO - 10.1109/FUZZ-IEEE.2017.8015532
M3 - Conference contribution
AN - SCOPUS:85030181792
T3 - IEEE International Conference on Fuzzy Systems
BT - 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
Y2 - 9 July 2017 through 12 July 2017
ER -