Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Cloud-based evolving intelligent method for weather time series prediction
AU - Almeida Soares, Eduardo
AU - Mota, Vania
AU - Poucas, Ricardo
AU - Leite, Daniel
PY - 2017/7/9
Y1 - 2017/7/9
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.
U2 - 10.1109/fuzz-ieee.2017.8015532
DO - 10.1109/fuzz-ieee.2017.8015532
M3 - Conference contribution/Paper
SN - 9781509060344
BT - 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PB - IEEE
ER -