Abstract
Parallel hybridization is one of the most well-established hybrid structures proposed in the literature. Since an unavoidable high degree of multi-collinearity (MC) exists among predictors in hybrid structures, the hybrid results and estimated parameters may not be generalizable and stable. Thus, the major innovation of this study lies in addressing this shortcoming that parallel hybrid models faced and eliminating the MC problem, which is not yet addressed in the literature on time series forecasting using hybridization methodologies. To solve this difficulty and analyze the effect of the MC phenomenon on the forecasting accuracy of the parallel hybrid model as well as the reliance degree of estimated weights and results, a new class of hybrid models is proposed based on parallel integration of principal component analysis (PCA), auto-regressive integrated moving average (ARIMA), multi-layer perceptron neural network (MLPNN) and exponential smoothing model (ESM). The forecasting accuracy and reliance degree of the proposed model is compared with the traditional parallel hybridization of ARIMA, MLPNN, and ESM models. The experimental results revealed that by removing MC, improved forecasting accuracy is obtained. Besides, the reliability and statistical power of results, specifically the estimated weights, are enhanced. The verification results indicate that the proposed PCA-ARIMA&MLP&ESM model is a powerful applicable tool for time series forecasting in terms of forecasting accuracy and model reliability.
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References
Hajirahimi Z, Khashei M (2019) Hybrid structures in time series modeling and forecasting: a review. Eng Appl Artif Intell 86:83–106
Thakur N, Karmakar S, Soni S (2022) Time series forecasting for uni-variant data using hybrid GA-OLSTM model and performance evaluations. Int J Inf Technol 14:1961–1966
Khashei M, Hajirahimi Z (2019) A comparative study of series arima/mlp hybrid models for stock price forecasting. Commun Stat Simul Comput 48:2625–2640
Jiang X, Zhang L, Chen X (2014) Short-term forecasting of high-speed rail demand: a hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China. Transp Res Part C 44:110–127
Kumar R, Kumar P, Kumar Y (2022) Multi-step time series analysis and forecasting strategy using ARIMA and evolutionary algorithms. Int J Inf Technol 14:359–373
Sharma N, Mangla M, Mohanty SN, Pattanaik CR (2021) Employing stacked ensemble approach for time series forecasting. Int J Inf Technol 9:2075–2080
Bates JM, Granger WJ (1996) The combination of forecasts. Oper Res 20:451–468
Haji Rahimi Z, Khashei M (2018) A least squares-based parallel hybridization of statistical and intelligent models for time series forecasting. Comput Ind Eng 118:44–53
Yang Y, Chen Y, Wang Y, Li C, Li L (2016) Modelling a combined method based on ANFIS and neural network improved by DE algorithm: a case study for short-term electricity demand forecasting. Appl Soft Comput 49:663–675
Safari A, Davallou M (2018) Oil price forecasting using a hybrid model. Energy 148:49–58
Gunst RF, Mason RL (1979) Some considerations in the evaluation of alternate prediction equations. Technometrics 21:55–63
Kumar D (2017) Feature selection for face recognition using DCT-PCA and Bat algorithm. Int J Inf Technol 9:411–423
Srijiranon K, Lertratanakham Y, Tanantong T (2022) A hybrid Framework Using PCA, EMD and LSTM methods for stock market price prediction with sentiment analysis. Appl Sci 12:10823
Zhang X, Wei Z (2019) A hybrid model based on principal component analysis, wavelet transform, and extreme learning machine optimized by bat algorithm for daily solar radiation forecasting. Sustainability 11:4138
Kristjanpoller W, Minutolo MC (2018) A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis. Expert Syst Appl 109:1–11
Feng C, Cui M, Hodge B-M, Zhang J (2019) A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. Appl Energy 190:1245–1257
Kong X, Liu X, Shi R, Lee KY (2015) Wind speed prediction using reduced support vector machines with feature selection. Neurocomputing 169:449–456
Davò F, Alessandrini S, Sperati S, DelleMonache L, Airoldi D, Vespucci MT (2016) Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting. Sol Energy 134:327–338
Zhang Y, Chen B, Pan G, Zhao Y (2019) A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting. Energy Convers Manag 195:180–197
Lan H, Zhang C, Yi Hong Y, He Y, Wen S (2019) Day-ahead spatiotemporal solar irradiation forecasting using frequency based hybrid principal component analysis and neural network. Appl Energy 247:389–402
Liu D, Sun K (2019) Random forest solar power forecast based on classification optimization. Energy 187:115940
Yadav V, Nath S (2019) Novel hybrid model for daily prediction of PM10 using principal component analysis and artificial neural network. Int J Environ Sci Technol 16:2839–2848
Franceschi F, Cobo M, Figueredo M (2018) Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using artificial neural networks, principal component analysis, and k-means clustering. Atmos Pollut Res 9:912–922
Malvoni M, DeGiorgi MG, Congedo PM (2016) Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data. Neurocomputing 211:72–83
Solgi A, Pourhaghi A, Bahmani R, Zarei H (2016) Improving SVR and ANFIS performance using wavelet transform and PCA algorithm for modeling and predicting biochemical oxygen demand (BOD). Ecohydrol Hydrobiol 17:164–175
Sánchez Lasheras F, García Nieto PJ, Javier de Cos Juez F, Mayo Bayan R, González Suárez VM (2015) A hybrid PCA-CART-MARS-based prognostic approach of the remaining useful life for aircraft engines. Sensors 15:7062–7083
Han L, Romero CE, Yao Zh (2015) Wind power forecasting based on principle component phase space reconstruction. Renew Energy 81:737–744
Hafezi R, Shahrabi J, Hadavandi E (2015) A bat-neural network multi-agent system (BNNMAS) for stock price prediction: case study of DAX stock price. Appl Soft Comput 29:196–210
Hajirahimi Z, Khashei M (2020) Sequence in hybridization of statistical and intelligent models in time series forecasting. Neural Process Lett 54:83–106
Hajirahimi Z, Khashei M (2022) Series hybridization of parallel (SHOP) models for time series forecasting. Physica A Stat Mech Appl 596:127173
Yudong Z, W, Lenan, (2009) Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Syst Appl 36:8849–8854
Kao LJ, Chiu CC, Lu CJ, Chang CH (2013) A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting. Decis Support Syst 54:1228–1244
Box GP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, San Francisco
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Hajirahimi, Z., Khashei, M. & Hamadani, A.Z. Principal component-based hybrid model for time series forecasting. Int. j. inf. tecnol. 15, 3045–3053 (2023). https://doi.org/10.1007/s41870-023-01343-2
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DOI: https://doi.org/10.1007/s41870-023-01343-2