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Ensemble of evolving data clouds and fuzzy models for weather time series prediction

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Ensemble of evolving data clouds and fuzzy models for weather time series prediction. / Soares, Eduardo; Costa Jr, Pyramo; Costa, Bruno; Leite, Daniel.

In: Applied Soft Computing, Vol. 64, 01.03.2018, p. 445-453.

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Soares, Eduardo ; Costa Jr, Pyramo ; Costa, Bruno ; Leite, Daniel. / Ensemble of evolving data clouds and fuzzy models for weather time series prediction. In: Applied Soft Computing. 2018 ; Vol. 64. pp. 445-453.

Bibtex

@article{d7a84575d90b47848285f0ceadd45dca,
title = "Ensemble of evolving data clouds and fuzzy models for weather time series prediction",
abstract = "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.",
keywords = "Ensemble learning, Data clouds, Evolving fuzzy systems, Weather time series prediction, Online data stream",
author = "Eduardo Soares and {Costa Jr}, Pyramo and Bruno Costa and Daniel Leite",
year = "2018",
month = mar,
day = "1",
doi = "10.1016/j.asoc.2017.12.032",
language = "English",
volume = "64",
pages = "445--453",
journal = "Applied Soft Computing",
issn = "1568-4946",
publisher = "Elsevier Science B.V.",

}

RIS

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 -