2016
DOI: 10.1515/jaiscr-2017-0001
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Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets

Abstract: This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete datasets. A benchmark of high roughness time series from Mackay Glass (MG), Logistic (LOG), Henon (HEN) and some univariate series chosen from NN3 Forecasting Competition are used. An average smoothing technique is assumed to complete the data missing in the dataset. The Hurst parameter estimated through wavelets is used to estimate the roughness of the real… Show more

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Cited by 18 publications
(7 citation statements)
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“…In this paper, a classical non-linear autoregressive model based on neural networks whose parameters are batch tuned [11] and its performance is evaluated by stochastic analysis is proposed [12].…”
Section: Proposed Approachmentioning
confidence: 99%
See 2 more Smart Citations
“…In this paper, a classical non-linear autoregressive model based on neural networks whose parameters are batch tuned [11] and its performance is evaluated by stochastic analysis is proposed [12].…”
Section: Proposed Approachmentioning
confidence: 99%
“…Several experiences had been obtained from previous works detailed in [12]. Here, an NN-based AR filter model is tuned.…”
Section: Nn-based Ar Modelmentioning
confidence: 99%
See 1 more Smart Citation
“…Moreover, it is used to credit risk management in banks [3], predicting the success of bank's direct marketing [4], analyzing consumer loyalty [5], sport [6], medicine [7] and many other areas. Prediction can be performed by various tools such as learning vector quantization [8], neuro-fuzzy systems [9], data stream classifiers [10], energy-associated tuning [11] or deep neural networks [12]. In the case where part of data is missing we can use rough set-based systems [13].…”
Section: Introductionmentioning
confidence: 99%
“…The authors used not only load data but also the information about the season and average temperature, and they were able to achieve a very low one-hour prediction rate. Authors of [9] predicted short-term incomplete data based on energy. Generally, NFSs can be used for numerous tasks related to classification and regression, e.g.…”
Section: Introductionmentioning
confidence: 99%