메뉴 건너뛰기




Volumn 13, Issue 3, 2018, Pages

Statistical and Machine Learning forecasting methods: Concerns and ways forward

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPETITION; FORECASTING; MACHINE LEARNING; STRESS; TIME SERIES ANALYSIS; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; NORMAL DISTRIBUTION; STATISTICAL MODEL; SUPPORT VECTOR MACHINE;

EID: 85044501351     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0194889     Document Type: Article
Times cited : (933)

References (81)
  • 1
    • 85018991558 scopus 로고    scopus 로고
    • The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms
    • Makridakis S. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures. 2017; 90:46-60. https://doi.org/10.1016/j.futures.2017.03.006
    • (2017) Futures , vol.90 , pp. 46-60
    • Makridakis, S.1
  • 2
    • 0003123930 scopus 로고    scopus 로고
    • Forecasting with artificial neural networks:: The state of the art
    • Zhang G, Eddy Patuwo B, Hu Y M. Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting. 1998; 14(1):35-62. https://doi.org/10.1016/S0169-2070(97)00044-7
    • (1998) International Journal of Forecasting , vol.14 , Issue.1 , pp. 35-62
    • Zhang, G.1    Eddy Patuwo, B.2    Hu, Y.M.3
  • 3
    • 56349131204 scopus 로고    scopus 로고
    • Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting
    • Hamzacebi C, Akay D, Kutay F. Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications. 2009; 36(2, Part 2):3839-3844. https://doi.org/10.1016/j.eswa.2008.02.042.
    • (2009) Expert Systems with Applications , vol.36 , Issue.2 , pp. 3839-3844
    • Hamzacebi, C.1    Akay, D.2    Kutay, F.3
  • 4
    • 84956802323 scopus 로고    scopus 로고
    • A tutorial survey of architectures, algorithms, and applications for deep learning-ERRATUM
    • Deng L. A tutorial survey of architectures, algorithms, and applications for deep learning-ERRATUM. APSIPA Transactions on Signal and Information Processing. 2014; 3. https://doi.org/10.1017/atsip. 2013.9
    • (2014) APSIPA Transactions on Signal and Information Processing , Issue.3
    • Deng, L.1
  • 5
    • 84957926960 scopus 로고    scopus 로고
    • A survey of randomized algorithms for training neural networks
    • Zhang L, Suganthan PN. A survey of randomized algorithms for training neural networks. Information Sciences. 2016; 364-365(Supplement C):146-155. https://doi.org/10.1016/j.ins.2016.01.039.
    • (2016) Information Sciences , vol.364-365 , pp. 146-155
    • Zhang, L.1    Suganthan, P.N.2
  • 6
    • 85022083603 scopus 로고    scopus 로고
    • Extreme learning machine based transfer learning algorithms: A survey
    • Salaken SM, Khosravi A, Nguyen T, Nahavandi S. Extreme learning machine based transfer learning algorithms: A survey. Neurocomputing. 2017; 267:516-524. https://doi.org/10.1016/j.neucom.2017.06. 037.
    • (2017) Neurocomputing , vol.267 , pp. 516-524
    • Salaken, S.M.1    Khosravi, A.2    Nguyen, T.3    Nahavandi, S.4
  • 7
    • 85030639721 scopus 로고    scopus 로고
    • Machine learning approaches for estimating commercial building energy consumption
    • Robinson C, Dilkina B, Hubbs J, Zhang W, Guhathakurta S, Brown MA, et al. Machine learning approaches for estimating commercial building energy consumption. Applied Energy. 2017; 208(Supplement C):889-904. https://doi.org/10.1016/j.apenergy.2017.09.060.
    • (2017) Applied Energy , vol.208 , pp. 889-904
    • Robinson, C.1    Dilkina, B.2    Hubbs, J.3    Zhang, W.4    Guhathakurta, S.5    Brown, M.A.6
  • 8
    • 85008622769 scopus 로고    scopus 로고
    • Machine learning methods for solar radiation forecasting: A review
    • Voyant C, Notton G, Kalogirou S, Nivet ML, Paoli C, Motte F, et al. Machine learning methods for solar radiation forecasting: A review. Renewable Energy. 2017; 105(Supplement C):569-582. https://doi.org/10.1016/j.renene.2016.12.095.
    • (2017) Renewable Energy , vol.105 , pp. 569-582
    • Voyant, C.1    Notton, G.2    Kalogirou, S.3    Nivet, M.L.4    Paoli, C.5    Motte, F.6
  • 9
    • 0000032342 scopus 로고    scopus 로고
    • How effective are neural networks at forecasting and prediction? A review and evaluation
    • Adya M, Collopy F. How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting. 1998; 17(56):481-495. https://doi.org/10.1002/(SICI)1099-131X (1998090)17:5/6%3C481::AID-FOR709%3E3.0.CO;2-Q
    • (1998) Journal of Forecasting , vol.17 , Issue.56 , pp. 481-495
    • Adya, M.1    Collopy, F.2
  • 10
    • 35248833159 scopus 로고
    • Neural networks Forecasting breakthrough or passing fad?
    • Chatfield C. Neural networks: Forecasting breakthrough or passing fad? International Journal of Forecasting. 1993; 9(1):1-3. http://dx.doi.org/10.1016/0169-2070(93)90043-M.
    • (1993) International Journal of Forecasting , vol.9 , Issue.1 , pp. 1-3
    • Chatfield, C.1
  • 11
    • 0004393134 scopus 로고
    • Connectionist approach to time series prediction: An empirical test
    • Sharda R, Patil RB. Connectionist approach to time series prediction: An empirical test. Journal of Intelligent Manufacturing. 1992; 3(1):317-323. https://doi.org/10.1007/BF01577272
    • (1992) Journal of Intelligent Manufacturing , vol.3 , Issue.1 , pp. 317-323
    • Sharda, R.1    Patil, R.B.2
  • 12
    • 79956357674 scopus 로고    scopus 로고
    • Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction
    • Crone SF, Hibon M, Nikolopoulos K. Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting. 2011; 27 (3):635-660. http://dx.doi.org/10.1016/j.ijforecast.2011.04.001.
    • (2011) International Journal of Forecasting , vol.27 , Issue.3 , pp. 635-660
    • Crone, S.F.1    Hibon, M.2    Nikolopoulos, K.3
  • 13
    • 0042711018 scopus 로고    scopus 로고
    • On the need for time series data mining benchmarks: A survey and empirical demonstration
    • Keogh E, Kasetty S. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Mining and Knowledge Discovery. 2003; 7(4):349-371. https://doi.org/10.1023/A:1024988512476
    • (2003) Data Mining and Knowledge Discovery , vol.7 , Issue.4 , pp. 349-371
    • Keogh, E.1    Kasetty, S.2
  • 15
    • 77956724793 scopus 로고    scopus 로고
    • An empirical comparison of machine learning models for time series forecasting
    • Ahmed NK, Atiya AF, Gayar NE, El-Shishiny H. An Empirical Comparison of Machine Learning Models for Time Series Forecasting. Econometric Reviews. 2010; 29(5-6):594-621. https://doi.org/10.1080/07474938.2010.481556
    • (2010) Econometric Reviews , vol.29 , Issue.5-6 , pp. 594-621
    • Ahmed, N.K.1    Atiya, A.F.2    Gayar, N.E.3    El-Shishiny, H.4
  • 16
    • 0034288942 scopus 로고    scopus 로고
    • The M3-Competition: Results, conclusions and implications
    • Makridakis S, Hibon M. The M3-Competition: results, conclusions and implications. International Journal of Forecasting. 2000; 16(4):451-476. https://doi.org/10.1016/S0169-2070(00)00057-1
    • (2000) International Journal of Forecasting , vol.16 , Issue.4 , pp. 451-476
    • Makridakis, S.1    Hibon, M.2
  • 18
    • 85016420579 scopus 로고    scopus 로고
    • Forecasting stochastic neural network based on financial empirical mode decomposition
    • PMID: 28364677
    • Wang J, Wang J. Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Networks. 2017; 90:8-20. https://doi.org/10.1016/j.neunet.2017.03.004. PMID: 28364677
    • (2017) Neural Networks , vol.90 , pp. 8-20
    • Wang, J.1    Wang, J.2
  • 19
    • 84927749443 scopus 로고    scopus 로고
    • Neural networks in business time series forecasting: Benefits and problems
    • Zhao L. Neural Networks In Business Time Series Forecasting: Benefits And Problems. Review of Business Information Systems (RBIS). 2009; 13(3):57-62.
    • (2009) Review of Business Information Systems (RBIS) , vol.13 , Issue.3 , pp. 57-62
    • Zhao, L.1
  • 20
    • 0034288490 scopus 로고    scopus 로고
    • The theta model: A decomposition approach to forecasting
    • Assimakopoulos V, Nikolopoulos K. The theta model: a decomposition approach to forecasting. International Journal of Forecasting. 2000; 16(4):521-530. https://doi.org/10.1016/S0169-2070(00)00066-2
    • (2000) International Journal of Forecasting , vol.16 , Issue.4 , pp. 521-530
    • Assimakopoulos, V.1    Nikolopoulos, K.2
  • 22
    • 33749523557 scopus 로고    scopus 로고
    • Exponential smoothing: The state of the art-Part II
    • Gardner ES. Exponential smoothing: The state of the art-Part II. International Journal of Forecasting. 2006; 22(4):637-666. https://doi.org/10.1016/j.ijforecast.2006.03.005
    • (2006) International Journal of Forecasting , vol.22 , Issue.4 , pp. 637-666
    • Gardner, E.S.1
  • 24
    • 84982135146 scopus 로고    scopus 로고
    • Predicting the direction of stock market index movement using an optimized artificial neural network model
    • Qiu M, Song Y. Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model. PLOS ONE. 2016; 11(5):1-11. https://doi.org/10.1371/journal.pone.0155133
    • (2016) PLOS ONE , vol.11 , Issue.5 , pp. 1-11
    • Qiu, M.1    Song, Y.2
  • 25
    • 84947289699 scopus 로고    scopus 로고
    • Forecasting macroeconomic variables using neural network models and three automated model selection techniques
    • Kock AB, Terä svirta T. Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques. Econometric Reviews. 2016; 35(8-10):1753-1779. https://doi.org/10.1080/07474938.2015.1035163
    • (2016) Econometric Reviews , vol.35 , Issue.8-10 , pp. 1753-1779
    • Kock, A.B.1    Teräsvirta, T.2
  • 26
    • 85020064625 scopus 로고    scopus 로고
    • Neural networks versus box-jenkins method for turnover forecasting: A case study on the romanian organisation
    • Gabor MR, Dorgo LA. Neural Networks Versus Box-Jenkins Method for Turnover Forecasting: a Case Study on the Romanian Organisation. Transformations in Business and Economics. 2017; 16(1):187-211.
    • (2017) Transformations in Business and Economics , vol.16 , Issue.1 , pp. 187-211
    • Gabor, M.R.1    Dorgo, L.A.2
  • 28
    • 4344591889 scopus 로고    scopus 로고
    • Neural network forecasting for seasonal and trend time series
    • Zhang GP, Qi M. Neural network forecasting for seasonal and trend time series. European Journal of Operational Research. 2005; 160(2):501-514. https://doi.org/10.1016/j.ejor.2003.08.037
    • (2005) European Journal of Operational Research , vol.160 , Issue.2 , pp. 501-514
    • Zhang, G.P.1    Qi, M.2
  • 31
    • 33749517168 scopus 로고    scopus 로고
    • Another look at measures of forecast accuracy
    • Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. International Journal of Forecasting. 2006; 22(4):679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001
    • (2006) International Journal of Forecasting , vol.22 , Issue.4 , pp. 679-688
    • Hyndman, R.J.1    Koehler, A.B.2
  • 32
    • 0007104074 scopus 로고    scopus 로고
    • On the asymmetry of the symmetric MAPE
    • Goodwin P, Lawton R. On the asymmetry of the symmetric MAPE. International Journal of Forecasting. 1999; 15(4):405-408. http://dx.doi.org/10.1016/S0169-2070(99)00007-2.
    • (1999) International Journal of Forecasting , vol.15 , Issue.4 , pp. 405-408
    • Goodwin, P.1    Lawton, R.2
  • 34
    • 84984477151 scopus 로고
    • Exponential smoothing: The state of the art
    • Gardner ES. Exponential smoothing: the state of the art. Journal of Forecasting. 1985; 4(1):1-28. https://doi.org/10.1002/for.3980040103
    • (1985) Journal of Forecasting , vol.4 , Issue.1 , pp. 1-28
    • Gardner, E.S.1
  • 35
    • 79956363043 scopus 로고    scopus 로고
    • Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition
    • Andrawis RR, Atiya AF, El-Shishiny H. Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition. International Journal of Forecasting. 2011; 27 (3):672-688. https://doi.org/10.1016/j.ijforecast.2010.09.005
    • (2011) International Journal of Forecasting , vol.27 , Issue.3 , pp. 672-688
    • Andrawis, R.R.1    Atiya, A.F.2    El-Shishiny, H.3
  • 36
    • 48749112805 scopus 로고    scopus 로고
    • Automatic time series forecasting: The forecast package for R
    • Hyndman R, Khandakar Y. Automatic time series forecasting: the forecast package for R. Journal of Statistical Software. 2008; 26(3):1-22.
    • (2008) Journal of Statistical Software , vol.26 , Issue.3 , pp. 1-22
    • Hyndman, R.1    Khandakar, Y.2
  • 37
    • 0036071568 scopus 로고    scopus 로고
    • A state space framework for automatic forecasting using exponential smoothing methods
    • Hyndman RJ, Koehler AB, Snyder RD, Grose S. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting. 2002; 18(3):439-454. https://doi.org/10.1016/S0169-2070(01)00110-8
    • (2002) International Journal of Forecasting , vol.18 , Issue.3 , pp. 439-454
    • Hyndman, R.J.1    Koehler, A.B.2    Snyder, R.D.3    Grose, S.4
  • 38
    • 0028401357 scopus 로고
    • Recurrent neural networks and robust time series prediction
    • PMID: 18267794
    • Connor JT, Martin RD, Atlas LE. Recurrent neural networks and robust time series prediction. IEEE transactions on neural networks. 1994; 5(2):240-54. https://doi.org/10.1109/72.279188 PMID: 18267794
    • (1994) IEEE Transactions on Neural Networks , vol.5 , Issue.2 , pp. 240-254
    • Connor, J.T.1    Martin, R.D.2    Atlas, L.E.3
  • 41
    • 0031573117 scopus 로고    scopus 로고
    • Long short-term memory
    • PMID: 9377276
    • Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation. 1997; 9(8):1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735 PMID: 9377276
    • (1997) Neural Computation , vol.9 , Issue.8 , pp. 1735-1780
    • Hochreiter, S.1    Schmidhuber, J.2
  • 42
    • 0023331258 scopus 로고
    • An introduction to computing with neural nets
    • Lippmann RP. An Introduction to Computing with Neural Nets. IEEE ASSP Magazine. 1987; 4(2):4-22. https://doi.org/10.1109/MASSP.1987.1165576
    • (1987) IEEE ASSP Magazine , vol.4 , Issue.2 , pp. 4-22
    • Lippmann, R.P.1
  • 43
    • 0027205884 scopus 로고
    • A scaled conjugate gradient algorithm for fast supervised learning
    • Młller M. A scaled conjugate gradient algorithm for fast supervised learning. Neural networks. 1993; 6:525-533. https://doi.org/10.1016/S0893-6080(05)80056-5
    • (1993) Neural Networks , vol.6 , pp. 525-533
    • Młller, M.1
  • 44
    • 84893467682 scopus 로고    scopus 로고
    • Neural network ensemble operators for time series forecasting
    • Kourentzes N, Barrow DK, Crone SF. Neural network ensemble operators for time series forecasting. Expert Systems with Applications. 2014; 41(9):4235-4244. https://doi.org/10.1016/j.eswa.2013.12.011
    • (2014) Expert Systems with Applications , vol.41 , Issue.9 , pp. 4235-4244
    • Kourentzes, N.1    Barrow, D.K.2    Crone, S.F.3
  • 45
    • 84857383259 scopus 로고    scopus 로고
    • Neural networks in r using the stuttgart neural network simulator: Rsnns
    • Bergmeir C, Benitez JM. Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software. 2012; 46(7):1-26. https://doi.org/10.18637/jss.v046.i07
    • (2012) Journal of Statistical Software , vol.46 , Issue.7 , pp. 1-26
    • Bergmeir, C.1    Benitez, J.M.2
  • 46
    • 0001025418 scopus 로고
    • Bayesian interpolation
    • MacKay DJC. Bayesian Interpolation. Neural Computation. 1992; 4(3):415-447. https://doi.org/10.1162/neco.1992.4.3.415
    • (1992) Neural Computation , vol.4 , Issue.3 , pp. 415-447
    • MacKay, D.J.C.1
  • 48
    • 0025536870 scopus 로고
    • Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
    • Nguyen D, Widrow B. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. IJCNN Int Joint Conf Neural Networks. 1990; 13:C21.
    • (1990) IJCNN Int Joint Conf Neural Networks , vol.13 , pp. 21
    • Nguyen, D.1    Widrow, B.2
  • 50
    • 0026254768 scopus 로고
    • A general regression neural network
    • PMID: 18282872
    • Specht DF. A general regression neural network. IEEE Transactions on Neural Networks. 1991; 2 (6):568-576. https://doi.org/10.1109/72.97934 PMID: 18282872
    • (1991) IEEE Transactions on Neural Networks , vol.2 , Issue.6 , pp. 568-576
    • Specht, D.F.1
  • 59
    • 26444565569 scopus 로고
    • Finding structure in time
    • Elman JL. Finding structure in time. Cognitive Science. 1990; 14(2):179-211. https://doi.org/10.1207/s15516709cog1402-1
    • (1990) Cognitive Science , vol.14 , Issue.2 , pp. 179-211
    • Elman, J.L.1
  • 60
    • 84971640658 scopus 로고    scopus 로고
    • Chollet F, et al. Keras; 2015. https://github.com/keras-team/keras.
    • (2015) Keras
    • Chollet, F.1
  • 62
    • 0003053374 scopus 로고
    • Research prospective on neural network forecasting
    • Gorr WL. Research prospective on neural network forecasting. International Journal of Forecasting. 1994; 10(1):1-4. https://doi.org/10.1016/0169-2070(94)90044-2
    • (1994) International Journal of Forecasting , vol.10 , Issue.1 , pp. 1-4
    • Gorr, W.L.1
  • 64
    • 0029409251 scopus 로고
    • Neural modeling for time series: A statistical stepwise method for weight elimination
    • PMID: 18263428
    • Cottrell M, Girard B, Girard Y, Mangeas M, Muller C. Neural Modeling for Time Series: A Statistical Stepwise Method for Weight Elimination. IEEE Transactions on Neural Networks. 1995; 6(6):1355-1364. https://doi.org/10.1109/72.471372 PMID: 18263428
    • (1995) IEEE Transactions on Neural Networks , vol.6 , Issue.6 , pp. 1355-1364
    • Cottrell, M.1    Girard, B.2    Girard, Y.3    Mangeas, M.4    Muller, C.5
  • 65
    • 49049143455 scopus 로고
    • Trends and random walks in macroeconmic time series. Some evidence and implications
    • Nelson CR, Plosser CR. Trends and random walks in macroeconmic time series. Some evidence and implications. Journal of Monetary Economics. 1982; 10(2):139-162. https://doi.org/10.1016/0304-3932 (82)90012-5
    • (1982) Journal of Monetary Economics , vol.10 , Issue.2 , pp. 139-162
    • Nelson, C.R.1    Plosser, C.R.2
  • 67
    • 0002605320 scopus 로고
    • Some quick sign tests for trend in location and dispersion
    • Cox DR, Stuart A. Some Quick Sign Tests for Trend in Location and Dispersion. Biometrika. 1955; 42(1-2):80-95. https://doi.org/10.2307/2333424
    • (1955) Biometrika , vol.42 , Issue.1-2 , pp. 80-95
    • Cox, D.R.1    Stuart, A.2
  • 74
    • 84866454518 scopus 로고    scopus 로고
    • On the forecasting accuracy of multivariate GARCH models
    • Laurent S, Rombouts JVK, Violante F. On the forecasting accuracy of multivariate GARCH models. Journal of Applied Econometrics. 2012; 27(6):934-955. https://doi.org/10.1002/jae.1248
    • (2012) Journal of Applied Econometrics , vol.27 , Issue.6 , pp. 934-955
    • Laurent, S.1    Rombouts, J.V.K.2    Violante, F.3
  • 75
    • 84930272613 scopus 로고    scopus 로고
    • Simple versus complex forecasting: The evidence
    • Green KC, Armstrong JS. Simple versus complex forecasting: The evidence. Journal of Business Research. 2015; 68(8):1678-1685. http://dx.doi.org/10.1016/j.jbusres.2015.03.026.
    • (2015) Journal of Business Research , vol.68 , Issue.8 , pp. 1678-1685
    • Green, K.C.1    Armstrong, J.S.2
  • 78
    • 85009115385 scopus 로고    scopus 로고
    • Visualising forecasting algorithm performance using time series instance spaces
    • Kang Y, Hyndman RJ, Smith-Miles K. Visualising forecasting algorithm performance using time series instance spaces. International Journal of Forecasting. 2017; 33(2):345-358. https://doi.org/10.1016/j.ijforecast.2016.09.004
    • (2017) International Journal of Forecasting , vol.33 , Issue.2 , pp. 345-358
    • Kang, Y.1    Hyndman, R.J.2    Smith-Miles, K.3
  • 80
    • 85010651075 scopus 로고    scopus 로고
    • A survey of deep neural network architectures and their applications
    • Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE. A survey of deep neural network architectures and their applications. Neurocomputing. 2017; 234(Supplement C):11-26. https://doi.org/10.1016/j.neucom.2016.12.038.
    • (2017) Neurocomputing , vol.234 , pp. 11-26
    • Liu, W.1    Wang, Z.2    Liu, X.3    Zeng, N.4    Liu, Y.5    Alsaadi, F.E.6
  • 81
    • 85029156229 scopus 로고    scopus 로고
    • Facial expression recognition via learning deep sparse autoencoders
    • Zeng N, Zhang H, Song B, Liu W, Li Y, Dobaie AM. Facial expression recognition via learning deep sparse autoencoders. Neurocomputing. 2018; 273(Supplement C):643-649. https://doi.org/10.1016/j.neucom.2017.08.043.
    • (2018) Neurocomputing , vol.273 , pp. 643-649
    • Zeng, N.1    Zhang, H.2    Song, B.3    Liu, W.4    Li, Y.5    Dobaie, A.M.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.