Locally recurrent neural networks for wind speed prediction using spatial correlation

Barbounis, T. G.; Theocharis, John B.

doi:10.1016/j.ins.2007.05.024

Cited by 168 publications

(53 citation statements)

References 40 publications

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“…Meltser, Shoham & Manevitz (1996), performed a weight adjustment of the network through the BFGS Quasi-Newton method (Broyden-Fletcher-GoldfarnShanno). Barbounis and Theocharis (2007), performed the weights updating using the identification of recursive error prediction (RPE). Yeung, Chan & Ng (2009), used a new training objective function to adjust the weights for a network with radial basis functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid system prediction for the stock market: The case of transitional markets

Ralević¹,

Glišović²,

Djakovic³

et al. 2017

Industrija

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid system prediction for the stock market: The case of transitional markets

Ralević¹,

Glišović²,

Djakovic³

et al. 2017

Industrija

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show abstract

“…Meltser et al [86], performed a weight adjustment of the network through the BFGS Quasi-Newton method (Broyden-Fletcher-Goldfarn-Shanno). Barbouinis and Theocharis [87], performed the weights updating using the identification of recursive error prediction (RPE). Yeung et al [88], used a new training objective function to adjust the weights for a network with radial basis functions.…”

Section: Historical Developmentmentioning

confidence: 99%

Theory and Background

Gaxiola¹,

Melín²,

Valdez³

2016

New Backpropagation Algorithm With Type-2 Fuzzy Weights for Neural Networks

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“…Overmars et al [44] demonstrated the presence of the spatial associations in the land use data of Ecuador at different spatial scales. Barbounis and Theocharis [8] used spatial auto-correlation to predict the wind speeds in wind farms. Yang et al [58] used spatial auto-correlation to analyze the changes in the spatial distribution patterns of population density.…”

Section: Introductionmentioning

confidence: 99%

Detecting nominal variables’ spatial associations using conditional probabilities of neighboring surface objects’ categories

Bai

et al. 2016

Information Sciences

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a b s t r a c tHow to automatically mining the spatial association patterns in spatial data is a challenging task in spatial data mining. In this paper, we propose three indices that represent the per-class, inter-class, and overall spatial associations of a nominal variable, which are based on the conditional probabilities of surface object categories. These indices represent relative quantities and are normalized to the region [−1, 1], which more accord with the intuitive cognition of people. We present some algorithms for detecting spatial associations that are based on these indices. The proposed method can be regarded as an extension of join count statistics and Transiogram. Several constructive examples were used to illustrate the advantages of the new method. Using two real data sets, vegetation types in Qingxian, Shanxi, China and neural tube birth defects in Heshun, Shanxi, China, we ran comparative experiments with other commonly used methods, including join count statistics, co-location quotient, and Q(m) statistics. The experimental results show that the proposed method can detect more subtle spatial associations, and is not sensitive to the sequence of neighbors.

show abstract

Locally recurrent neural networks for wind speed prediction using spatial correlation

Cited by 168 publications

References 40 publications

Hybrid system prediction for the stock market: The case of transitional markets

Hybrid system prediction for the stock market: The case of transitional markets

Theory and Background

Detecting nominal variables’ spatial associations using conditional probabilities of neighboring surface objects’ categories

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