Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices
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[1] Gautam Bandyopadhyay,et al. Gold Price Forecasting Using ARIMA Model , 2015 .
[2] Ping-Feng Pai,et al. Revenue forecasting using a least-squares support vector regression model in a fuzzy environment , 2013, Inf. Sci..
[3] Chiun-Sin Lin,et al. Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting , 2012 .
[4] Hu Wang,et al. Probability-based least square support vector regression metamodeling technique for crashworthiness optimization problems , 2011 .
[5] Zhen Yang,et al. Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity , 2010 .
[6] Lijun Yang,et al. Particle swarm optimization-least squares support vector regression based forecasting model on dissolved gases in oil-filled power transformers , 2011 .
[7] Zichen Zhang,et al. A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting , 2018 .
[8] Mingchih Chen,et al. The Integration of Artificial Neural Networks and Text Mining to Forecast Gold Futures Prices , 2016, Commun. Stat. Simul. Comput..
[9] Renato De Leone,et al. Integrating support vector machines and neural networks , 2007, Neural Networks.
[10] Wei-Chiang Hong,et al. Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model , 2018, Applied Energy.
[11] Stephen F. Witt,et al. Modeling and Forecasting Demand in Tourism , 1991 .
[12] Reza Hafezi,et al. Forecasting Gold Price Changes: Application of an Equipped Artificial Neural Network , 2018 .
[13] Hanbo Zheng,et al. A novel model based on wavelet LS-SVM integrated improved PSO algorithm for forecasting of dissolved gas contents in power transformers , 2018 .
[14] Radha Krishna Prasad,et al. Application of ANN technique to predict the performance of solar collector systems - A review , 2018 .
[15] Ahmad Maliki Omar,et al. Least square support vector machine technique for short term solar irradiance forecasting , 2019, APPLIED PHYSICS OF CONDENSED MATTER (APCOM 2019).
[16] Chunmei Liu. Price Forecast for Gold Futures Based on GA-BP Neural Network , 2009, 2009 International Conference on Management and Service Science.
[17] Francisco Herrera,et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..
[18] Naren Ramakrishnan,et al. Forex-foreteller: currency trend modeling using news articles , 2013, KDD.
[19] Akash Dutt Dubey,et al. Gold price prediction using support vector regression and ANFIS models , 2016, 2016 International Conference on Computer Communication and Informatics (ICCCI).
[20] Ling Tang,et al. LSSVR ensemble learning with uncertain parameters for crude oil price forecasting , 2017, Appl. Soft Comput..
[21] Fong-Ching Yuan,et al. Using least square support vector regression with genetic algorithm to forecast beta systematic risk , 2015, J. Comput. Sci..
[22] Ling Tang,et al. A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting , 2016, Neural Computing and Applications.
[23] E. Zavadskas,et al. Forecasting gold price changes by using adaptive network fuzzy inference system , 2012 .
[24] Serkan Aras,et al. A new model selection strategy in time series forecasting with artificial neural networks: IHTS , 2016, Neurocomputing.
[25] Ping-Feng Pai,et al. Tourism demand forecasting using novel hybrid system , 2014, Expert Syst. Appl..
[26] Suresh Kumar Goyal,et al. An application of tournament genetic algorithm in a marketing oriented economic production lot-size model for deteriorating items , 2009 .
[27] Ertuğrul Çam,et al. Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines , 2015 .
[28] Yuhanis Yusof,et al. A review on optimization of least squares support vector machine for time series forecasting , 2016 .
[29] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[30] Kin Keung Lai,et al. A dynamic meta-learning rate-based model for gold market forecasting , 2012, Expert Syst. Appl..
[31] Jianzhou Wang,et al. The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China , 2015 .
[32] Ling Tang,et al. A novel hybrid FA-Based LSSVR learning paradigm for hydropower consumption forecasting , 2015, J. Syst. Sci. Complex..
[33] N. Soonthornphisaj,et al. Apply decision tree and support vector regression to predict the gold price , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[34] Mohd Wazir Mustafa,et al. Reactive power tracing in pool-based power system utilising the hybrid genetic algorithm and least squares support vector machine , 2012 .
[35] Feng Chen,et al. Least Square-Support Vector Regression based car-following model with sparse sample selection , 2010, 2010 8th World Congress on Intelligent Control and Automation.
[36] H. Shareef,et al. An application of Genetic Algorithm and Least Squares Support Vector Machine for tracing the transmission loss in deregulated power system , 2011, 2011 5th International Power Engineering and Optimization Conference.
[37] Kin Keung Lai,et al. An Improved EMD Online Learning-Based Model for Gold Market Forecasting , 2011 .
[38] Yi Ren,et al. An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed , 2020, Wind Engineering.
[39] Haiyan Song,et al. Recent Developments in Econometric Modeling and Forecasting , 2005 .
[40] Saman K. Halgamuge,et al. Combining News and Technical Indicators in Daily Stock Price Trends Prediction , 2007, ISNN.
[41] Antonino Parisi,et al. Forecasting gold price changes: Rolling and recursive neural network models , 2008 .
[42] Francisco Herrera,et al. A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.
[43] Shahrum Shah Abdullah,et al. Gold price prediction using radial basis function neural network , 2011, 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization.
[44] Xue-feng Yan,et al. Adaptive weighted least square support vector machine regression integrated with outlier detection and its application in QSAR , 2009 .
[45] Yuhanis Yusof,et al. Optimizing LSSVM Using ABC For Non-Volatile Financial Prediction , 2011 .
[46] Vida Varahrami,et al. Recognition of good prediction of gold price between MLFF and GMDH neural network , 2011 .
[47] Kesten C. Green,et al. Demand Forecasting: Evidence-Based Methods , 2005 .
[48] Emmanuel Sirimal Silva,et al. Forecasting the price of gold , 2015 .
[49] G. Barreto,et al. Novel sparse LSSVR models in primal weight space for robust system identification with outliers , 2017, Journal of Process Control.
[50] Siti Sakira Kamaruddin,et al. Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting , 2014, J. Comput. Sci..
[51] Sungzoon Cho,et al. Approximating support vector machine with artificial neural network for fast prediction , 2014, Expert Syst. Appl..
[52] Binghui Wu,et al. The fractal feature and price trend in the gold future market at the Shanghai Futures Exchange (SFE) , 2017 .
[53] Mohammad Reza Sarajian,et al. The comparison of NN, SVR, LSSVR and ANFIS at modeling meteorological and remotely sensed drought indices over the eastern district of Isfahan, Iran , 2017, Natural Hazards.
[54] Yang Zhang,et al. Novel chaotic bat algorithm for forecasting complex motion of floating platforms , 2019, Applied Mathematical Modelling.
[55] Tao Peng,et al. A Small Scale Forecasting Algorithm for Network Traffic based on Relevant Local Least Squares Support Vector Machine Regression Model , 2015 .
[56] Johan A. K. Suykens,et al. Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression , 2011, IEEE Transactions on Neural Networks.
[57] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[58] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.