Copper Metal Price Using Chaotic Time Series Forecating

Carrasco, Raúl; Vargas, Manuel; Soto, Ismael; Fuertes, Guillermo; Alfaro, Miguel

doi:10.1109/tla.2015.7164223

Cited by 24 publications

(11 citation statements)

References 18 publications

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“…Also, prevention, training and research as preventive measures become important for recovering the valuable vegetation heritage Chile offers. Implementation of forecasting methods [31,32], combined with different tools for trend breaks in time series [33], requires further work. Moreover, to gain better understanding of the spatio-temporal dynamics [34] of forest fires in the area considered here, spatial analysis is necessary over the long term.…”

Section: Discussionmentioning

confidence: 99%

Ecological Impact of Forest Fires and Subsequent Restoration in Chile

2018

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This note analyzes the effects forest fires in Chile have on vegetation and subsequent ecological restoration. We analyze why forest fires have been a main factor that affects the environment and causes the ecosystem to deteriorate, leading to loss of native forests, species extinction, damage to the urban population, and others. The data examined are derived from fire hotspots in Chile's central and central-south zones (33 • 00 S-41 • 57 S) between 1985 and 2017. We also analyze some key aspects for restoration priorities such as studying affected areas and posterior consequences. Finally, we evaluate actions the country has already taken, and propose further appropriate preventive and restoration measures.

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Section: Discussionmentioning

confidence: 99%

Ecological Impact of Forest Fires and Subsequent Restoration in Chile

2018

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“…The studied temporary series were able to be reconstructed. With the graphic methodology called graphic recurrence plot (Figure 9), it is possible to confirm the chaotic determinism of the wind series and the earthquakes through the patterns not homogeneously distributed [52][53][54]. Finally, the analysis of the time series using the analytical techniques (1) power Fourier spectrum, (2) spectrogram, (3) graphic recurrence plot, and (4) a positive Lyapunov exponent characterizes a deterministic time series associated with a system dynamic.…”

Section: Complexitymentioning

confidence: 99%

The Effect of Entropy on the Performance of Modified Genetic Algorithm Using Earthquake and Wind Time Series

et al. 2018

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The dynamic complexity of time series of natural phenomena allowed to improve the performance of the genetic algorithm to optimize the test mathematical functions. The initial populations of stochastic origin of the genetic algorithm were replaced using the series of time of winds and earthquakes. The determinism of the time series brings in more information in the search of the global optimum of the functions, achieving reductions of time and an improvement of the results. The information of the initial populations was measured using the entropy of Shannon and allowed to establish the importance of the entropy in the initial populations and its relation with getting better results. This research establishes a new methodology for using determinism time series to search the best performance of the models of optimization of genetic algorithms (GA).

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“…Several studies include copper and other metals as one of the products of interest in the evaluations of the prediction to improve the forecasts of price. Such studies employ different methods and mathematical models such as ARIMA models combined with wavelets [5], meta-heuristics models [6] and neural networks models [2,7,8]. On the other hand, The Fourier Transform [9] is used to analyze the variability of the prices of various metals.…”

Section: Introductionmentioning

confidence: 99%

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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Copper Metal Price Using Chaotic Time Series Forecating

Cited by 24 publications

References 18 publications

Ecological Impact of Forest Fires and Subsequent Restoration in Chile

Ecological Impact of Forest Fires and Subsequent Restoration in Chile

The Effect of Entropy on the Performance of Modified Genetic Algorithm Using Earthquake and Wind Time Series

Copper Price Prediction Using Support Vector Regression Technique

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