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Journal ArticleDOI

Forecasting copper prices by decision tree learning

TLDR
In this paper, a machine learning algorithm based on decision tree was used to predict future copper prices, with mean absolute percentage errors below 4% in both short-term and long-term.
About
This article is published in Resources Policy.The article was published on 2017-06-01. It has received 79 citations till now. The article focuses on the topics: Metal prices & Decision tree.

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Citations
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Journal ArticleDOI

Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network

TL;DR: A novel hybrid deep learning model is presented, which combines the VMD (variational mode decomposition) method and the LSTM (long short-term memory) network to construct a forecasting model, which has superior performance for non-ferrous metals price forecasting.
Journal ArticleDOI

Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm

TL;DR: A novel model for accurately forecasting long-term monthly gold price fluctuations using a recent meta-heuristic method called whale optimization algorithm (WOA) as a trainer to learn the multilayer perceptron neural network (NN), which demonstrates the superiority of the hybrid WOA–NN model over other models.
Journal ArticleDOI

Review of Supported Pd-Based Membranes Preparation by Electroless Plating for Ultra-Pure Hydrogen Production.

TL;DR: In this review, the most relevant advances in the preparation of supported Pd-based membranes for hydrogen production in recent years are presented, mainly focused in the incorporation of the hydrogen selective layer (palladium or palladium-based alloy) by the electroless plating.
Journal ArticleDOI

Copper price estimation using bat algorithm

TL;DR: In this paper, the Bat algorithm was used to predict the copper price volatility, and the determined equation with 0.132 of RMSE was used, which is better than the classic estimation methods.
Journal ArticleDOI

Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility

TL;DR: A novel model for accurately forecasting monthly iron ore price volatilities is proposed that integrates chaotic behavior into a recent meta-heuristic method grasshopper optimization algorithm to form a new GOA algorithm called chaotic grasshoppers optimization algorithm (CGOA), which is used as a trainer to learn the multilayer perceptron neural network (NN).
References
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Journal ArticleDOI

Classification and regression trees

TL;DR: This article gives an introduction to the subject of classification and regression trees by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.
Book

Classification and regression trees

Leo Breiman
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Journal ArticleDOI

Note on Regression and Inheritance in the Case of Two Parents

TL;DR: In this paper, the authors consider a population in which sexual selection and natural selection may or may not be taking place, and assume only that the deviations from the mean in the case of any organ of any generation follow exactly or closely the normal law of frequency.
Journal ArticleDOI

Measuring firm performance using financial ratios: A decision tree approach

TL;DR: Sensitivity analyses indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables, and the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy.
Related Papers (5)
Trending Questions (1)
Will copper prices ever go down?

We showed that our method is capable of accurately and reliably predicting copper prices in both short-term (days) and long-term (years), with mean absolute percentage errors below 4%.