Notice of Violation of IEEE Publication Principles: A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market

Areekul, Phatchakorn; Senjyu, Tomonobu; Toyama, Hirofumi; Yona, Atsushi

doi:10.1109/tpwrs.2009.2036488

Cited by 124 publications

(67 citation statements)

References 21 publications

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“…This confirms that simple network structure that has a small number of hidden nodes often works well in out-of-sample forecasting [16]- [20]. This can be due to the over fitting problem in neural network modeling process that allows the established network to fit the training data well, but poor generalization may happen.…”

Section: Journal Of Economics Business and Management Vol 3 No 7supporting

confidence: 62%

Modeling Gold Price via Artificial Neural Network

Mombeini¹,

Yazdani–Chamzini²

2015

JOEBM

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Section: Journal Of Economics Business and Management Vol 3 No 7supporting

confidence: 62%

Modeling Gold Price via Artificial Neural Network

Mombeini¹,

Yazdani–Chamzini²

2015

JOEBM

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“…Applying generalized mathematical formulation with parameters such as , ARIMA model can make a non-stationary time series become stationary. ARIMA model is based on the assumption of linearity of the predicted values [19].…”

Section: Arima Modelmentioning

confidence: 99%

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

Mohammed

Wang

2017

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multipoint prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient.Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

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“…In [25] past volatility is calculated as standard deviation of arithmetic and logarithmic return over a time window T. If p t is a spot price for a commodity at the time t, arithmetic return over time period h is defined as:…”

Section: Price Volatilitymentioning

confidence: 99%

Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

Cerjan¹,

Matijaš

Delimar

2014

Energies

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Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI) is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX) electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.

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Notice of Violation of IEEE Publication Principles: A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market

Cited by 124 publications

References 21 publications

Modeling Gold Price via Artificial Neural Network

Modeling Gold Price via Artificial Neural Network

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

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