Copper price estimation using bat algorithm

Dehghani, Hesam; Bogdanović, Dejan

doi:10.1016/j.resourpol.2017.10.015

Cited by 75 publications

(36 citation statements)

References 17 publications

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“…Our results show that the presented model is able to predict copper price volatilities near reality. Similar results are obtained in other works, such as the work presented in [31]. In this work, a bat algorithm was used [32] to determine the coefficients of time series functions to predict the behaviour of the same time series of this work, but exclusively during the year 2016.…”

Section: Discussionsupporting

confidence: 76%

Copper Price Prediction Using Support Vector Regression Technique

Astudillo¹,

Carrasco²,

Fernández‐Campusano³

et al. 2020

Preprint

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Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is non-linear, non-stationary, and which have periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of trend-cycle. In this paper, we study a copper price prediction method using Support Vector Regression. This work explores the potential of the Support Vector Regression with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchanges. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data-sets, our results obtained indicate that the parameters (C, ε, γ) of the model Support Vector Regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, being the RMSE equal or less than the 2.2% for prediction periods of 5 and 10 days.

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Section: Discussionsupporting

confidence: 76%

Copper Price Prediction Using Support Vector Regression Technique

Astudillo¹,

Carrasco²,

Fernández‐Campusano³

et al. 2020

Preprint

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show abstract

“…BPNN forecasting model is organized in three layer which are input, hidden and output layer. Therefore, the number of hidden layers was set to two and population count was set to 20 [26]. Then, SVM forecasting model are developed to compare with BPNN model.…”

Section: Comparison Of Developed Forecasting Modelmentioning

confidence: 99%

“…Another efficient optimization algorithm is Bat algorithm (BA) proposed by Yang, (2010) [25]. BA is based on echolocation capability of microbats that guide them to find their food [26,27]. Various study on implementation of SVM-PSO can be found in literature such as Sudheer et al, [2] studied forecasting of streamflow considering streamflow and rainfall as the input parameters, Wang et al [28] conducted rainfall forecasting and Xuan et al [29] studied forecasting of water quality.…”

Section: Introductionmentioning

confidence: 99%

“…At present, there are small number of applications of BA in streamflow forecasting. However, BA is successfully implemented in other field such as price forecasting [24,26], output power prediction [30] and optimization [31,27]. This study focuses on the application of support vector machine and neural network based model to forecast monthly streamflow for the study dataset at Kuantan River, Peninsular Malaysia.…”

Section: Introductionmentioning

confidence: 99%

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Support Vector Machine and Neural Network based Model for Monthly Stream Flow Forecasting

Zaini¹,

Malek²,

Yusoff³

et al. 2018

IJET

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Accurate forecasting of streamflow is desired in many water resources planning and management, flood prevention and design development. In this study, the accuracy of two hybrid model, support vector machine - particle swarm optimization (SVM-PSO) and bat algorithm – backpropagation neural network (BA-BPNN) for monthly streamflow forecasting at Kuantan River located in Peninsular Malaysia are investigated and compared to regular SVM and BPNN model. Heuristic optimization namely PSO and BA are introduced to find the optimum SVM and BPNN parameters. The input parameters to the forecasting models are antecedent streamflow, historical rainfall and meteorological parameters namely evaporation, temperature, relative humidity and mean wind speed. Two performance evaluation measure, root mean square error (RMSE) and coefficient of determination (R2) were employed to evaluate the performance of developed forecasting model. It is found that, RMSE and R2 for hybrid SVM-PSO are 24.8267 m3/s and 0.9651 respectively while general SVM model yields RMSE of 27.5086 m3/s and 0.9305 of R2 for testing phase. Besides that, hybrid BA-BPNN produces RMSE, 17.7579 m3/s and R2, 0.7740 while BPNN model produces lower RMSE and R2 of 28.1396 m3/s and 0.5015 respectively. Therefore, the results indicate that hybrid model, SVM-PSO and Bat-BPNN yield better performance as compared to general SVM and BPNN, respectively in streamflow forecasting.

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“…Mathematical modelling is one part of the commodity price forecasting and contributes to developing the new branches of solvability of these still current problems [1][2][3][4]. In mathematical models forecasting the prices on the commodity exchanges, the statistical methods are often used [5][6][7][8][9][10][11]. Our prognostic numerical models were based on the numerical solution of the Cauchy initial problem for the 1st order ordinary differential equations [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Influence of the Price Movements to the Accuracy Within Numerical Price Forecasting

Lascsáková¹

2019

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The paper is aimed at commodity price forecasting using a numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To acquire significant forecasting improvement, the idea of the modification of the initial condition value was realized. By having analysed the forecasting success of determined numerical models, it was found out that commodity price evolution affected the accuracy of the price forecasting. The absolute percentage prognoses errors were usually lower at a stable price increase and when price fluctuation appeared. Therefore, prognoses calculated without changing initial condition value were satisfying. Within significant changes in the price evolution and at a rapid price increase, the prognoses acquired higher absolute percentage errors. That caused replacing the initial condition value by the nearest stock exchange. Consider this strategy, the following calculated prognoses got closer to the forecast stock exchanges and price forecasting became more advantageous with respect to the price course.

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Copper price estimation using bat algorithm

Cited by 75 publications

References 17 publications

Copper Price Prediction Using Support Vector Regression Technique

Copper Price Prediction Using Support Vector Regression Technique

Support Vector Machine and Neural Network based Model for Monthly Stream Flow Forecasting

Influence of the Price Movements to the Accuracy Within Numerical Price Forecasting

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