Access the full text.
Sign up today, get DeepDyve free for 14 days.
R. Clemen (1989)
Combining forecasts: A review and annotated bibliographyInternational Journal of Forecasting, 5
Mohanad Al-Musaylh, R. Deo, Yan Li, J. Adamowski (2018)
Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecastingApplied Energy, 217
C. Fan, F. Xiao, Shengwei Wang (2014)
Development of prediction models for next-day building energy consumption and peak power demand using data mining techniquesApplied Energy, 127
Yulin Han, Lu Wang, Jindong Gao, Z. Xing, Tao Tao (2017)
Combination forecasting based on SVM and neural network for urban rail vehicle spare parts demand2017 36th Chinese Control Conference (CCC)
J. Bates, C. Granger (1969)
The Combination of ForecastsJournal of the Operational Research Society, 20
OR, 20
Luis Aburto, R. Weber (2007)
Improved supply chain management based on hybrid demand forecastsAppl. Soft Comput., 7
Huai Su, E. Zio, Jinjun Zhang, Mingjing Xu, Xueyi Li, Zongjie Zhang (2019)
A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN modelEnergy
C. Hamzaçebi (2008)
Improving artificial neural networks' performance in seasonal time series forecastingInf. Sci., 178
Niematallah Elamin, M. Fukushige (2018)
Modeling and forecasting hourly electricity demand by SARIMAX with interactionsEnergy
R. Steenbergen, M. Mes (2020)
Forecasting demand profiles of new productsDecis. Support Syst., 139
Xuan Zhang, H. Jiang, Y. Zhang (2012)
The Hybrid Method to Predict Biochemical Oxygen Demand of Haihe River in ChinaAdvanced Materials Research, 610-613
Eric Jnr, Yao Ziggah, S. Relvas (2021)
Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecastingSustainable Cities and Society, 66
IEEE Int. Conf. Neural Networks, 4
A. Ghasemi, H. Shayeghi, M. Moradzadeh, M. Nooshyar (2016)
A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side managementApplied Energy, 177
G. Zhang (2003)
Time series forecasting using a hybrid ARIMA and neural network modelNeurocomputing, 50
S. Cang (2014)
A Comparative Analysis of Three Types of Tourism Demand Forecasting Models: Individual, Linear Combination and Non‐linear CombinationInternational Journal of Tourism Research, 16
Alexander Hess, S. Spinler, M. Winkenbach (2021)
Real-time demand forecasting for an urban delivery platformTransportation Research Part E-logistics and Transportation Review, 145
Fang-Mei Tseng, Hsiao-Cheng Yu, G. Tzeng (2002)
Combining neural network model with seasonal time series ARIMA modelTechnological Forecasting and Social Change, 69
M. Barassi, Yuqian Zhao (2017)
Combination Forecasting of Energy Demand in the UKThe Energy Journal, 39
Shiwei Yu, Kejun Zhu (2012)
A hybrid procedure for energy demand forecasting in ChinaEnergy, 37
Jung-Hua Wang, Jia-Yann Leu (1996)
Stock market trend prediction using ARIMA-based neural networksProceedings of International Conference on Neural Networks (ICNN'96), 4
Fang-Mei Tseng, G. Tzeng, Hsiao-Cheng Yu (1999)
Fuzzy Seasonal Time Series for Forecasting the Production Value of the Mechanical Industry in TaiwanTechnological Forecasting and Social Change, 60
P. Jiang, Xiao Liu, Meimei Zheng (2019)
Emergency Blood Demand Forecasting after EarthquakesIFAC-PapersOnLine
Zhongsheng Hua, Bin Zhang (2006)
A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare partsAppl. Math. Comput., 181
Qimin Zhang, N. Chen, Junchao Zhao (2010)
Combined Forecast Model of Refined Oil Demand Based on Grey Theory2010 International Symposium on Intelligence Information Processing and Trusted Computing
Shujie Shen, Gang Li, Haiyan Song (2008)
An Assessment of Combining Tourism Demand Forecasts over Different Time HorizonsJournal of Travel Research, 47
J. Bedi, Durga Toshniwal (2019)
Deep learning framework to forecast electricity demandApplied Energy
N. Arunraj, Diane Ahrens (2015)
A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecastingInternational Journal of Production Economics, 170
Yi Yang, Yanhua Chen, Yachen Wang, Caihong Li, Lian Li (2016)
Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecastingAppl. Soft Comput., 49
T. Hill, M. O'Connor, W. Remus (1996)
Neural Network Models for Time Series ForecastsManagement Science, 42
Fang-Mei Tseng, G. Tzeng (2002)
A fuzzy seasonal ARIMA model for forecastingFuzzy Sets Syst., 126
Kuan-Yu Chen (2011)
Combining linear and nonlinear model in forecasting tourism demandExpert Syst. Appl., 38
A. Laouafi, M. Mordjaoui, S. Haddad, T. Boukelia, A. Ganouche (2017)
Online electricity demand forecasting based on an effective forecast combination methodologyElectric Power Systems Research, 148
A. Timmermann (2005)
Forecast CombinationsCEPR Discussion Paper Series
M. Joy, Simon Jones (2005)
predicting bed demand in a hospital using neural networks and ARIMA models: a hybrid approach
Yasmine Rashed, H. Meersman, C. Sys, E. Voorde, T. Vanelslander (2018)
A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of portsTransportation Research Part A: Policy and Practice
Chih-Hsuan Wang, Jen-Yu Chen (2019)
Demand forecasting and financial estimation considering the interactive dynamics of semiconductor supply-chain companiesComput. Ind. Eng., 138
B. Brentan, E. Luvizotto, M. Herrera, J. Izquierdo, R. Pérez-García (2017)
Hybrid regression model for near real-time urban water demand forecastingJ. Comput. Appl. Math., 309
N. Riahi, Seyyed-Mahdi Hosseini-Motlagh, B. Teimourpour (2013)
A Three-phase Hybrid Times Series Modeling Framework for Improved Hospital Inventory Demand Forecastinternational journal of hospital research, 2
F. Chahkoutahi, M. Khashei (2017)
A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecastingEnergy, 140
R. Mohammadi, S. Ghomi, F. Zeinali (2014)
A new hybrid evolutionary based RBF networks method for forecasting time series: A case study of forecasting emergency supply demand time seriesEng. Appl. Artif. Intell., 36
M. Khashei, M. Bijari, S. Hejazi (2012)
Combining seasonal ARIMA models with computational intelligence techniques for time series forecastingSoft Computing, 16
Song Ding (2018)
A novel self-adapting intelligent grey model for forecasting China's natural-gas demandEnergy
Gang Xie, Yatong Qian, Shouyang Wang (2021)
Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approachTourism Management, 82
Ping-Feng Pai, Kuo-Chen Hung, Kuo-Ping Lin (2014)
Tourism demand forecasting using novel hybrid systemExpert Syst. Appl., 41
Shuojiang Xu, H. Chan, Tiantian Zhang (2019)
Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approachTransportation Research Part E: Logistics and Transportation Review
The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting.Design/methodology/approachThe main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components.FindingsEmpirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed.Originality/valueTo the best of the authors’ knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed.
Journal of Modelling in Management – Emerald Publishing
Published: Nov 29, 2022
Keywords: Artificial intelligence; Modeling; Forecasting; Neural networks; Linear models; Parallel and series hybrid strategies; Seasonal multilayer perceptrons (SMLPs); Seasonal autoregressive integrated moving average (SARIMA); Demand forecasting
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.