A comparison of artificial neural network and time series models for forecasting commodity prices

Kohzadi, Nowrouz; Boyd, Milton S.; Kermanshahi, B.; Kaastra, Iebeling

doi:10.1016/0925-2312(95)00020-8

Cited by 229 publications

(97 citation statements)

References 22 publications

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“…A comparison of neural networks and ARIMA models to forecast commodity prices showed that neural network forecasts were more accurate than ARIMA forecasts. Moreover, the success of ARIMA models is conditional upon the underlying data generating process being linear, while neural networks can account for nonlinear relationships [29]. Hybrid methodologies, that combine neural networks and ARIMA models, have been also proposed [30] to take advantage of the unique strength of each model in linear and nonlinear modeling.…”

Section: Introductionmentioning

confidence: 99%

Short-term electricity prices forecasting in a competitive market: A neural network approach

Catalão

Mariano

Mendes

et al. 2007

Electric Power Systems Research

356

178

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This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California.

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Section: Introductionmentioning

confidence: 99%

Short-term electricity prices forecasting in a competitive market: A neural network approach

Catalão

Mariano

Mendes

et al. 2007

Electric Power Systems Research

356

178

View full text Add to dashboard Cite

show abstract

“…There are numerous studies to compare the performances of ANN and traditional time series techniques. For example, the empirical results in Ansuj et al (1996), Caire et al (1992), Chin and Arthur (1996), Hill and O'Connor (1996), Kohzadi et al (1996), Maier and Dandy (1996) showed that the ANN gave improved results in terms of forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Statistical Methods and Artificial Neural Networks

Mammadov

Yazıcı

Yolaçan

et al. 2005

J. Mod. App. Stat. Meth.

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“…well suited for prediction purposes. Kohzadi, et al (1996) compared neural network and ARIMA models to forecast US monthly live cattle and wheat cash prices. Results showed the neural network forecasts were considerably more accurate than those of the traditional ARIMA models, which were used as a benchmark.…”

Section: Review Of Ann Literaturementioning

confidence: 99%

The Efficiency of Artificial Neural Networks for Forecasting in the Presence of Autocorrelated Disturbances

White¹,

Safi²

2016

IJSP

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We compare three forecasting methods, Artificial Neural Networks (ANNs), Autoregressive Integrated Moving Average (ARIMA) and Regression models. Using computer simulations, the major finding reveals that in the presence of autocorrelated errors ANNs perform favorably compared to ARIMA and regression for nonlinear models. The model accuracy for ANN is evaluated by comparing the simulated forecast results with the real data for unemployment in Palestine which were found to be in excellent agreement.

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A comparison of artificial neural network and time series models for forecasting commodity prices

Cited by 229 publications

References 22 publications

Short-term electricity prices forecasting in a competitive market: A neural network approach

Short-term electricity prices forecasting in a competitive market: A neural network approach

Statistical Methods and Artificial Neural Networks

The Efficiency of Artificial Neural Networks for Forecasting in the Presence of Autocorrelated Disturbances

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