Access the full text.
Sign up today, get DeepDyve free for 14 days.
Ratree Kummong, S. Supratid (2016)
Thailand tourism forecasting based on a hybrid of discrete wavelet decomposition and NARX neural networkInd. Manag. Data Syst., 116
D. Rumelhart, Geoffrey Hinton, Ronald Williams (1986)
Learning representations by back-propagating errorsNature, 323
A. Panda, S. Nanda (2018)
Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economiesInternational Journal of Managerial Finance, 14
F. Diebold, R. Mariano (1994)
Comparing Predictive AccuracyJournal of Business & Economic Statistics, 20
S. Gupta, Sachin Kashyap (2016)
Modelling volatility and forecasting of exchange rate of British pound sterling and Indian rupeeJournal of Modelling in Management, 11
F. Chang, Pin-An Chen, Yinghua Lu, Eric Huang, Kai-Yao Chang (2014)
Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood controlJournal of Hydrology, 517
A. Parlos, Omar Rais, A. Atiya (1999)
Multi-step-ahead prediction using dynamic recurrent neural networksIJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1
(2013)
Maximum overlap discrete wavelet methods in modeling banking data
Jonathan Ticknor (2013)
A Bayesian regularized artificial neural network for stock market forecastingExpert Syst. Appl., 40
A. Belayneh, J. Adamowski, B. Khalil, B. Ozga-Zieliński (2014)
Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression modelsJournal of Hydrology, 508
J. Elman (1990)
Finding Structure in TimeCogn. Sci., 14
Sheng Chen, S. Billings, P. Grant (1990)
Non-linear system identification using neural networksInternational Journal of Control, 51
J. Fourier (2009)
Théorie analytique de la chaleur
D. MacKay (1992)
A Practical Bayesian Framework for Backpropagation NetworksNeural Computation, 4
I. Tijani, Rini Akmeliawati, A. Legowo, A. Budiyono (2014)
Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolutionEng. Appl. Artif. Intell., 33
G. Nason, R. Sachs (1999)
Wavelets in time-series analysisPhilosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 357
A. El-Shafie, A. Noureldin, M. Taha, A. Hussain, M. Mukhlisin (2011)
Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, MalaysiaHydrology and Earth System Sciences, 16
Eui-Bang Lee, Jinwha Kim, Sang-Gun Lee (2017)
Predicting customer churn in mobile industry using data mining technologyInd. Manag. Data Syst., 117
Ö. Kisi, M. Çimen (2012)
Precipitation forecasting by using wavelet-support vector machine conjunction modelEng. Appl. Artif. Intell., 25
I. Leontaritis, S. Billings (1985)
Input-output parametric models for non-linear systems Part II: stochastic non-linear systemsInternational Journal of Control, 41
H. Drucker, C. Burges, L. Kaufman, Alex Smola, V. Vapnik (1996)
Support Vector Regression Machines
D. Gabor (1946)
Theory of communication. Part 1: The analysis of informationJournal of the Institution of Electrical Engineers - Part III: Radio and Communication Engineering, 93
Yuebiao Li, Zhiheng Li, Mao-jing Jin, S. Yin (2013)
Multiple-step ahead Traffic Forecasting based on GMM Embedded BP NetworkProcedia - Social and Behavioral Sciences, 96
David Harvey, S. Leybourne, Emily Whitehouse (2017)
Forecast evaluation tests and negative long-run variance estimates in small samplesInternational Journal of Forecasting, 33
(1995)
Gaussian-mixture basis function networks for nonlinear signal processing
K. Narendra, K. Parthasarathy (1990)
Identification and control of dynamical systems using neural networksIEEE transactions on neural networks, 1 1
J. Jang (1993)
ANFIS: adaptive-network-based fuzzy inference systemIEEE Trans. Syst. Man Cybern., 23
International Journal of Control, 41
Sanjita Jaipuria, S. Mahapatra (2019)
A study on behaviour of bullwhip effect in (R, S) inventory control system considering DWT-MGGP demand forecasting modelJournal of Modelling in Management
Lezama Palomino, J. Carlos. (2020)
Efecto del mercado de futuros en la volatilidad del mercado SPOT: caso aplicado al mercado accionario colombiano
Dhanya Jothimani, R. Shankar, Surendra Yadav (2015)
Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on National Stock Exchange Fifty IndexJournal of Financial Management and analysis, 28
R.J. Hyndman (1993)
Time series data library
J. Filho, C. Affonso, R. Oliveira (2014)
Energy price prediction multi-step ahead using hybrid model in the Brazilian marketElectric Power Systems Research, 117
Yukun Bao, T. Xiong, Zhongyi Hu (2014)
Multi-step-ahead time series prediction using multiple-output support vector regressionNeurocomputing, 129
H. Badrzadeh, R. Sarukkalige, A. Jayawardena (2013)
Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecastingJournal of Hydrology, 507
V. Papathanasopoulou, Ioulia Markou, C. Antoniou (2016)
Online calibration for microscopic traffic simulation and dynamic multi-step prediction of traffic speedTransportation Research Part C-emerging Technologies, 68
Yong Song, Yibin Li, Qun Wang, Cai-hong Li (2010)
Multi-steps prediction of chaotic time series based on echo state network2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)
Georgia Papacharalampous, Hristos Tyralis (2018)
Evaluation of random forests and Prophet for daily streamflow forecastingAdvances in Geosciences
Pin-An Chen, Li-Chiu Chang, F. Chang (2013)
Reinforced recurrent neural networks for multi-step-ahead flood forecastsJournal of Hydrology, 497
Neural Networks, 13
T. Bollerslev (1986)
Generalized autoregressive conditional heteroskedasticityJournal of Econometrics, 31
J. Nash, J. Sutcliffe (1970)
River flow forecasting through conceptual models part I — A discussion of principles☆Journal of Hydrology, 10
G. Beriha, B. Patnaik, S. Mahapatra (2012)
Assessment of occupational health practices in Indian industriesJournal of Modelling in Management, 7
S. Mallat (1989)
A Theory for Multiresolution Signal Decomposition: The Wavelet RepresentationIEEE Trans. Pattern Anal. Mach. Intell., 11
Deyun Wang, Deyun Wang, Hongyuan Luo, O. Grunder, Yanbing Lin, Haixiang Guo (2017)
Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithmApplied Energy, 190
B. Horne, C. Giles (1994)
An experimental comparison of recurrent neural networks
An accurate long-term multi-step forecast provides crucial basic information for planning and reinforcing managerial decision-support. However, nonstationarity and nonlinearity, normally consisted of several types of managerial data can seriously ruin the forecasting computation. This paper aims to propose an effective long-term multi-step forecasting conjunction model, namely, wavelet–nonlinear autoregressive neural network (WNAR) conjunction model. The WNAR combines discrete wavelet transform (DWT) and nonlinear autoregressive neural network (NAR) to cope with such nonstationarity and nonlinearity within the managerial data; as a consequence, provides insight information that enhances accuracy and reliability of long-term multi-step perspective, leading to effective management decision-making.Design/methodology/approachBased on WNAR conjunction model, wavelet decomposition is executed for efficiently extracting hidden significant, temporal features contained in each of six benchmark nonstationary data sets from different managerial domains. Then, each extracted feature set at a particular resolution level is fed into NAR for the further forecast. Finally, NAR forecasting results are reconstructed. Forecasting performance measures throughout 1 to 30-time lags rely on mean absolute percentage error (MAPE), root mean square error (RMSE), Nash-Sutcliffe efficiency index or the coefficient of efficiency (Ef) and Diebold–Mariano (DM) test. An effect of data characteristic in terms of autocorrelation on forecasting performances of each data set are observed.FindingsLong-term multi-step forecasting results show the best accuracy and high-reliability performance of the proposed WNAR conjunction model over some other efficient forecasting models including a single NAR model. This is confirmed by DM test, especially for the short-forecasting horizon. In addition, rather steady, effective long-term multi-step forecasting performances are yielded with slight effect from time lag changes especially for the data sets having particular high autocorrelation, relative against 95 per cent degree of confidence normal distribution bounds.Research limitations/implicationsThe WNAR, which combines DWT with NAR can be accounted as a bridge for the gap between machine learning, engineering signal processing and management decision-support systems. Thus, WNAR is referred to as a forecasting tool that provides insight long-term information for managerial practices. However, in practice, suitable exogenous input forecast factors are required on the managerial domain-by-domain basis to correctly foresee and effectively prepare necessary reasonable management activities.Originality/valueFew works have been implemented to handle the nonstationarity, consisted of nonlinear managerial data to attain high-accurate long-term multi-step forecast. Combining DWT and NAR capabilities would comprehensively and specifically deal with the nonstationarity and nonlinearity difficulties at once. In addition, it is found that the proposed WNAR yields rather steady, effective long-term multi-step forecasting performance throughout specific long time lags regarding the data, having certainly high autocorrelation levels across such long time lags.
Journal of Modelling in Management – Emerald Publishing
Published: Oct 4, 2019
Keywords: Information systems; Time series; Artificial intelligence; Modelling; Decision analysis; Neural networks; Discrete wavelet transform; Nonlinear autoregressive neural network; Long-term multi-step forecast; Autocorrelation; Conjunction model; Nonstationarity
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.