A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM
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[1] Hamidreza Zareipour,et al. A New Feature Selection Technique for Load and Price Forecast of Electrical Power Systems , 2017, IEEE Transactions on Power Systems.
[2] F. Diebold,et al. Comparing Predictive Accuracy , 1994, Business Cycles.
[3] Amir Hossein Gandomi,et al. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.
[4] Ying-Yi Hong,et al. Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network , 2012 .
[5] T. W. Anderson,et al. Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes , 1952 .
[6] E. S. Gardner. EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .
[7] Saeid Sanei,et al. Extracting fetal heart signal from noisy maternal ECG by multivariate singular spectrum analysis , 2010 .
[8] İsmail Durgun,et al. Structural Design Optimization of Vehicle Components Using Cuckoo Search Algorithm , 2012 .
[9] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[10] C. Rodriguez,et al. Energy price forecasting in the Ontario competitive power system market , 2004, IEEE Transactions on Power Systems.
[11] N. Amjady,et al. Energy price forecasting - problems and proposals for such predictions , 2006 .
[12] T. Dillon,et al. Electricity price short-term forecasting using artificial neural networks , 1999 .
[13] Jing Ma,et al. Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting , 2017 .
[14] P. Luh,et al. Energy clearing price prediction and confidence interval estimation with cascaded neural networks , 2002 .
[15] Paras Mandal,et al. Sensitivity analysis of neural network parameters to improve the performance of electricity price forecasting , 2009 .
[16] Najeh Chaâbane,et al. A novel auto-regressive fractionally integrated moving average–least-squares support vector machine model for electricity spot prices prediction , 2014 .
[17] W. Fuller,et al. Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .
[18] V M F Mendes,et al. Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting , 2011, IEEE Transactions on Power Systems.
[19] Florian Ziel,et al. Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday Structure , 2015, IEEE Transactions on Power Systems.
[20] Olivier Grunder,et al. Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm , 2017 .
[21] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[22] A. Zhigljavsky,et al. Forecasting European industrial production with singular spectrum analysis , 2009 .
[23] Ying-Yi Hong,et al. A neuro-fuzzy price forecasting approach in deregulated electricity markets , 2005 .
[24] Jianzhou Wang,et al. Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system , 2019, Renewable Energy.
[25] N. Bigdeli,et al. Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA) , 2011 .
[26] Xin-She Yang,et al. Multiobjective cuckoo search for design optimization , 2013, Comput. Oper. Res..
[27] Ioannis P. Panapakidis,et al. Day-ahead electricity price forecasting via the application of artificial neural network based models , 2016 .
[28] R. Vautard,et al. Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series , 1989 .
[29] N. Amjady,et al. Day-Ahead Price Forecasting of Electricity Markets by Mutual Information Technique and Cascaded Neuro-Evolutionary Algorithm , 2009, IEEE Transactions on Power Systems.
[30] J. Bai,et al. Forecasting economic time series using targeted predictors , 2008 .
[31] Xiaobo Zhang,et al. A novel decomposition‐ensemble model for forecasting short‐term load‐time series with multiple seasonal patterns , 2018, Appl. Soft Comput..
[32] Ashwani Kumar,et al. Electricity price forecasting in deregulated markets: A review and evaluation , 2009 .
[33] Rosa Espínola,et al. The effect of wind generation and weekday on Spanish electricity spot price forecasting , 2011 .
[34] Rob J Hyndman,et al. Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing , 2011 .
[35] Jiahan Li,et al. Forecasting Macroeconomic Time Series: LASSO-Based Approaches and Their Forecast Combinations with Dynamic Factor Models , 2014 .
[36] Rafał Weron,et al. Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks , 2018, 1805.06649.
[37] Tao Hong,et al. Improving short term load forecast accuracy via combining sister forecasts , 2016 .
[38] Chen-Ching Liu,et al. Day-Ahead Electricity Price Forecasting in a Grid Environment , 2007, IEEE Transactions on Power Systems.
[39] Mohammad Moradzadeh,et al. A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management , 2016 .
[40] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[41] Irena Koprinska,et al. Feature Selection for Interval Forecasting of Electricity Demand Time Series Data , 2015 .
[42] R. Weron. Electricity price forecasting: A review of the state-of-the-art with a look into the future , 2014 .
[43] In-Keun Yu,et al. Prediction of system marginal price of electricity using wavelet transform analysis , 2002 .
[44] Farshid Keynia,et al. Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method , 2008 .
[45] Anil K. Bera,et al. Efficient tests for normality, homoscedasticity and serial independence of regression residuals , 1980 .
[46] C. García-Martos,et al. Mixed Models for Short-Run Forecasting of Electricity Prices: Application for the Spanish Market , 2007, IEEE Transactions on Power Systems.
[47] Jianzhou Wang,et al. Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling , 2010 .
[48] Hossein Hassani,et al. Singular Spectrum Analysis: Methodology and Comparison , 2021, Journal of Data Science.
[49] Marco van Akkeren,et al. A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.
[50] Jianzhou Wang,et al. Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm , 2016 .
[51] Nitin Singh,et al. Short term electricity price forecast based on environmentally adapted generalized neuron , 2017 .
[52] Bart De Schutter,et al. Forecasting day-ahead electricity prices in Europe: the importance of considering market integration , 2017, ArXiv.
[53] P. Phillips. Testing for a Unit Root in Time Series Regression , 1988 .
[54] Anatoly A. Zhigljavsky,et al. Singular Spectrum Analysis for Time Series , 2013, International Encyclopedia of Statistical Science.
[55] Farshid Keynia,et al. Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network , 2010 .
[56] Farshid Keynia. A new feature selection algorithm and composite neural network for electricity price forecasting , 2012, Eng. Appl. Artif. Intell..
[57] P. Luh,et al. Improving market clearing price prediction by using a committee machine of neural networks , 2004, IEEE Transactions on Power Systems.
[58] Z. Tan,et al. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models , 2010 .
[59] A.J. Conejo,et al. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.
[60] J. Contreras,et al. Forecasting next-day electricity prices by time series models , 2002 .
[61] Bart De Schutter,et al. Forecasting spot electricity prices Deep learning approaches and empirical comparison of traditional algorithms , 2018 .