Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Ensemble of evolving data clouds and fuzzy models for weather time series prediction
AU - Soares, Eduardo
AU - Costa Jr, Pyramo
AU - Costa, Bruno
AU - Leite, Daniel
PY - 2018/3/1
Y1 - 2018/3/1
N2 - This paper describes a variation of data cloud-based intelligent method known as typicality-and-eccentricity-based method for data analysis (TEDA). The objective is to develop data-centric nonlinear and time-varying models to predict mean monthly temperature. TEDA is an incremental algorithm that considers the data density and scattering of clouds over the data space. The method does not require a priori knowledge of the dataset and user-defined parameters. However, if some knowledge about the number of clouds and rules is available, then it can be expressed through a single parameter. Past values of minimum, maximum and mean monthly temperature, as well as previous values of exogenous variables such as cloudiness, rainfall and humidity are 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 Brazilian cities such as Sao Paulo, Manaus, Porto Alegre, and Natal. These cities are known to have particular weather characteristics. TEDA results are compared with results provided by the evolving Takagi–Sugeno (eTS) and the extended Takagi–Sugeno (xTS) methods. Additionally, an ensemble of cloud and fuzzy models and fuzzy aggregation operators is developed to give single-valued and granular predictions of the time series. Granular predictions convey a range of possible temperature values and give an idea about the error and uncertainty associated with the data.
AB - This paper describes a variation of data cloud-based intelligent method known as typicality-and-eccentricity-based method for data analysis (TEDA). The objective is to develop data-centric nonlinear and time-varying models to predict mean monthly temperature. TEDA is an incremental algorithm that considers the data density and scattering of clouds over the data space. The method does not require a priori knowledge of the dataset and user-defined parameters. However, if some knowledge about the number of clouds and rules is available, then it can be expressed through a single parameter. Past values of minimum, maximum and mean monthly temperature, as well as previous values of exogenous variables such as cloudiness, rainfall and humidity are 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 Brazilian cities such as Sao Paulo, Manaus, Porto Alegre, and Natal. These cities are known to have particular weather characteristics. TEDA results are compared with results provided by the evolving Takagi–Sugeno (eTS) and the extended Takagi–Sugeno (xTS) methods. Additionally, an ensemble of cloud and fuzzy models and fuzzy aggregation operators is developed to give single-valued and granular predictions of the time series. Granular predictions convey a range of possible temperature values and give an idea about the error and uncertainty associated with the data.
KW - Ensemble learning
KW - Data clouds
KW - Evolving fuzzy systems
KW - Weather time series prediction
KW - Online data stream
U2 - 10.1016/j.asoc.2017.12.032
DO - 10.1016/j.asoc.2017.12.032
M3 - Journal article
VL - 64
SP - 445
EP - 453
JO - Applied Soft Computing
JF - Applied Soft Computing
SN - 1568-4946
ER -