4.7 Article

Forecasting monthly copper price: A comparative study of various machine learning-based methods

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Interdisciplinary Applications

A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET

Hongquan Guo et al.

Summary: A new technique, SVRs-GLMNET, was proposed to accurately predict the distance of fly-rock in open-pit mines by combining SVR models and a GLMNET model. The study divided the dataset into training, validating, and testing sets, ultimately showing that SVRs-GLMNET outperformed other models in predicting fly-rock distance.

ENGINEERING WITH COMPUTERS (2021)

Article Computer Science, Artificial Intelligence

A comparative analysis of gradient boosting algorithms

Candice Bentejac et al.

Summary: The family of gradient boosting algorithms has been expanded with XGBoost, LightGBM, and CatBoost, which focus on reliability, efficiency, speed, and accuracy. In the comparison study, CatBoost is the best in generalization accuracy and AUC, LightGBM is the fastest but not the most accurate, and XGBoost ranks second in accuracy and training speed.

ARTIFICIAL INTELLIGENCE REVIEW (2021)

Article Computer Science, Artificial Intelligence

A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam

Hoang Nguyen et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Computer Science, Interdisciplinary Applications

Prediction of ultimate bearing capacity through various novel evolutionary and neural network models

Hossein Moayedi et al.

ENGINEERING WITH COMPUTERS (2020)

Article Geosciences, Multidisciplinary

Estimation of Blast-Induced Air Overpressure in Quarry Mines Using Cubist-Based Genetic Algorithm

Qiancheng Fang et al.

NATURAL RESOURCES RESEARCH (2020)

Article Environmental Studies

A random walk through the trees: Forecasting copper prices using decision learning methods

Juan D. Diaz et al.

RESOURCES POLICY (2020)

Proceedings Paper Computer Science, Artificial Intelligence

A New Workload Prediction Model Using Extreme Learning Machine and Enhanced Tug of War optimization

Thieu Nguyen et al.

11TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 3RD INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS (2020)

Article Business

Machine learning solutions to challenges in finance: An application to the pricing of financial products

Lirong Gan et al.

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE (2020)

Article Geosciences, Multidisciplinary

Forecasting Copper Prices Using Hybrid Adaptive Neuro-Fuzzy Inference System and Genetic Algorithms

Zakaria Alameer et al.

NATURAL RESOURCES RESEARCH (2019)

Article Environmental Sciences

Use of copper, silver and zinc nanoparticles against foliar and soil-borne plant pathogens

Anastasios A. Malandrakis et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2019)

Article Computer Science, Artificial Intelligence

An adaptive forecasting approach for copper price volatility through hybrid and non-hybrid models

Diego Garcia et al.

APPLIED SOFT COMPUTING (2019)

Article Business

Forecasting of advertising effectiveness for renewable energy technologies: A neural network analysis

Mehdi Sharifi et al.

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE (2019)

Article Computer Science, Artificial Intelligence

Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization

Thieu Nguyen et al.

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS (2019)

Article Environmental Studies

Forecasting base metal prices with the Chilean exchange rate

Pablo Pincheira Brown et al.

RESOURCES POLICY (2019)

Article Construction & Building Technology

Gradient boosting machine for modeling the energy consumption of commercial buildings

Samir Touzani et al.

ENERGY AND BUILDINGS (2018)

Article Environmental Studies

Copper price estimation using bat algorithm

Hesam Dehghani et al.

RESOURCES POLICY (2018)

Article Business

Early identification of emerging technologies: A machine learning approach using multiple patent indicators

Changyong Lee et al.

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE (2018)

Article Computer Science, Artificial Intelligence

Random forests-based extreme learning machine ensemble for multi-regime time series prediction

Lin Lin et al.

EXPERT SYSTEMS WITH APPLICATIONS (2017)

Article Environmental Sciences

Characterizing copper flows in international trade of China, 1975-2015

Ling Zhang et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2017)

Article Computer Science, Artificial Intelligence

Random Forests for Big Data

Robin Genuer et al.

BIG DATA RESEARCH (2017)

Article Environmental Studies

Forecasting copper prices by decision tree learning

Chang Liu et al.

RESOURCES POLICY (2017)

Article Computer Science, Artificial Intelligence

A novel version of k nearest neighbor: Dependent nearest neighbor

Omer Faruk Ertugrul et al.

APPLIED SOFT COMPUTING (2017)

Article Environmental Sciences

Modeling river discharge time series using support vector machine and artificial neural networks

Mohammad Ali Ghorbani et al.

ENVIRONMENTAL EARTH SCIENCES (2016)

Article Environmental Sciences

Copper demand, supply, and associated energy use to 2050

Ayman Elshkaki et al.

GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS (2016)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Environmental Studies

Forecasting the COMEX copper spot price by means of neural networks and ARIMA models

Fernando Sanchez Lasheras et al.

RESOURCES POLICY (2015)

Proceedings Paper Management

The chaotic relationship between oil return, gold, silver and copper returns in TURKEY: Non-Linear ARDL and Augmented Non-linear Granger Causality

Melike Bildirici et al.

Proceedings of the 4th International Conference on Leadership, Technology, Innovation and Business Management (ICLTIBM-2014) (2015)

Article Computer Science, Artificial Intelligence

A combination of artificial neural network and random walk models for financial time series forecasting

Ratnadip Adhikari et al.

NEURAL COMPUTING & APPLICATIONS (2014)

Review Computer Science, Artificial Intelligence

A review of unsupervised feature learning and deep learning for time-series modeling

Martin Langkvist et al.

PATTERN RECOGNITION LETTERS (2014)

Article Environmental Studies

An improved wavelet-ARIMA approach for forecasting metal prices

Thomas Kriechbaumer et al.

RESOURCES POLICY (2014)

Article Business

Dynamic scenario discovery under deep uncertainty: The future of copper

Jan H. Kwakkel et al.

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE (2013)

Article Computer Science, Artificial Intelligence

Gradient boosting trees for auto insurance loss cost modeling and prediction

Leo Guelman

EXPERT SYSTEMS WITH APPLICATIONS (2012)

Article Biotechnology & Applied Microbiology

What is a support vector machine?

William S. Noble

NATURE BIOTECHNOLOGY (2006)

Article Computer Science, Interdisciplinary Applications

Structural damage detection using neural network with learning rate improvement

X Fang et al.

COMPUTERS & STRUCTURES (2005)

Article Computer Science, Artificial Intelligence

Support vector machine with adaptive parameters in financial time series forecasting

LJ Cao et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2003)

Article Engineering, Chemical

Bioleaching of copper ore flotation concentrates

Z Sadowski et al.

MINERALS ENGINEERING (2003)

Article Computer Science, Interdisciplinary Applications

Stochastic gradient boosting

JH Friedman

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)