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Volumn 122, Issue , 2013, Pages 375-385

Models of performance of time series forecasters

Author keywords

Algorithm selection problem; Forecasting; Performance prediction; Time series; Time series features

Indexed keywords

ACCURATE PREDICTION; ALGORITHM SELECTION; AUTOMATIC PROCEDURES; CLASSIFICATION TASKS; LINEAR COMBINATIONS; PERFORMANCE PREDICTION; TIME SERIES FEATURES; TIME SERIES FORECASTING;

EID: 84884201967     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.05.035     Document Type: Article
Times cited : (14)

References (49)
  • 1
    • 0034288943 scopus 로고    scopus 로고
    • An application of rule-based forecasting to a situation lacking domain knowledge
    • Adya M., Armstrong J., Collopy F., Kennedy M. An application of rule-based forecasting to a situation lacking domain knowledge. Int. J. Forecast. 2000, 16(October (4)):477-484.
    • (2000) Int. J. Forecast. , vol.16 , Issue.4 OCTOBER , pp. 477-484
    • Adya, M.1    Armstrong, J.2    Collopy, F.3    Kennedy, M.4
  • 2
    • 0035315158 scopus 로고    scopus 로고
    • Automatic identification of time series features for rule-based forecasting
    • Adya M., Collopy F., Armstrong J., Kennedy M. Automatic identification of time series features for rule-based forecasting. Int. J. Forecast. 2001, 17(April (2)):143-157.
    • (2001) Int. J. Forecast. , vol.17 , Issue.2 APRIL , pp. 143-157
    • Adya, M.1    Collopy, F.2    Armstrong, J.3    Kennedy, M.4
  • 3
    • 0016355478 scopus 로고
    • A new look at the statistical model identification
    • Akaike H. A new look at the statistical model identification. IEEE Trans. Autom. Control 1974, 19(December (6)):716-723.
    • (1974) IEEE Trans. Autom. Control , vol.19 , Issue.6 DECEMBER , pp. 716-723
    • Akaike, H.1
  • 5
    • 0042047333 scopus 로고
    • Selecting appropriate forecasting models using rule induction
    • Arinze B. Selecting appropriate forecasting models using rule induction. Omega 1994, 22(November (6)):647-658.
    • (1994) Omega , vol.22 , Issue.6 NOVEMBER , pp. 647-658
    • Arinze, B.1
  • 6
    • 0031141263 scopus 로고    scopus 로고
    • Combining and selecting forecasting models using rule based induction
    • Arinze B., Kim S.-L., Anandarajan M. Combining and selecting forecasting models using rule based induction. Comput. Oper. Res. 1997, 24(May (5)):423-433.
    • (1997) Comput. Oper. Res. , vol.24 , Issue.5 MAY , pp. 423-433
    • Arinze, B.1    Kim, S.-L.2    Anandarajan, M.3
  • 7
    • 84884210176 scopus 로고
    • An analysis of transformations, J. R. Stat. Soc. Ser. B (Methodol.)
    • G.E.P. Box, D.R. Cox, An analysis of transformations, J. R. Stat. Soc. Ser. B (Methodol.) (1964) 211-252.
    • (1964) , pp. 211-252
    • Box, G.E.P.1    Cox, D.R.2
  • 9
    • 0000167087 scopus 로고
    • Rule-based forecasting. development and validation of an expert systems approach to combining time series extrapolations
    • Collopy F., Armstrong J.S. Rule-based forecasting. development and validation of an expert systems approach to combining time series extrapolations. Manage. Sci. 1992, 38(October (10)):1394-1414.
    • (1992) Manage. Sci. , vol.38 , Issue.10 OCTOBER , pp. 1394-1414
    • Collopy, F.1    Armstrong, J.S.2
  • 10
    • 84855999011 scopus 로고    scopus 로고
    • Forecasting time series with complex seasonal patterns using exponential smoothing
    • De Livera A.M., Hyndman R.J., Snyder R.D. Forecasting time series with complex seasonal patterns using exponential smoothing. J. Am. Stat. Assoc. 2011, 106(December (496)):1513-1527.
    • (2011) J. Am. Stat. Assoc. , vol.106 , Issue.496 DECEMBER , pp. 1513-1527
    • De Livera, A.M.1    Hyndman, R.J.2    Snyder, R.D.3
  • 13
    • 3242708140 scopus 로고    scopus 로고
    • Least angle regression
    • Efron B. Least angle regression. Ann. Stat. 2004, 32(2):407-499.
    • (2004) Ann. Stat. , vol.32 , Issue.2 , pp. 407-499
    • Efron, B.1
  • 15
    • 84887181344 scopus 로고    scopus 로고
    • Models of performance of evolutionary program induction algorithms based on indicators of problem difficulty, Evol. Comput. (November), in press
    • M. Graff, R. Poli, J.J. Flores, Models of performance of evolutionary program induction algorithms based on indicators of problem difficulty, Evol. Comput. (November) (2012) http://dx.doi.org/10.1162/EVCO_a_00096, in press.
    • (2012)
    • Graff, M.1    Poli, R.2    Flores, J.J.3
  • 17
    • 33750380289 scopus 로고    scopus 로고
    • Performance prediction and automated tuning of randomized and parametric algorithms, CP
    • F. Hutter, Y. Hamadi, H.H. Hoos, K. Leyton-Brown, Performance prediction and automated tuning of randomized and parametric algorithms, in: CP, 2006, pp. 213-228.
    • (2006) , pp. 213-228
    • Hutter, F.1    Hamadi, Y.2    Hoos, H.H.3    Leyton-Brown, K.4
  • 18
    • 43849093314 scopus 로고    scopus 로고
    • The admissible parameter space for exponential smoothing models
    • Hyndman R., Akram M., Archibald B. The admissible parameter space for exponential smoothing models. Ann. Inst. Stat. Math. 2008, 60(2):407-426.
    • (2008) Ann. Inst. Stat. Math. , vol.60 , Issue.2 , pp. 407-426
    • Hyndman, R.1    Akram, M.2    Archibald, B.3
  • 19
    • 84884208774 scopus 로고    scopus 로고
    • Forecasting with Exponential Smoothing: The State Space Approach, Springer, August
    • R. Hyndman, A.B. Koehler, J.K. Ord, R.D. Snyder, Forecasting with Exponential Smoothing: The State Space Approach, Springer, August 2008.
    • (2008)
    • Hyndman, R.1    Koehler, A.B.2    Ord, J.K.3    Snyder, R.D.4
  • 20
    • 48749112805 scopus 로고    scopus 로고
    • Automatic time series forecasting. the forecast package for R
    • Hyndman R.J., Khandakar Y. Automatic time series forecasting. the forecast package for R. J. Stat. Software 2008, 27(3):1-22.
    • (2008) J. Stat. Software , vol.27 , Issue.3 , pp. 1-22
    • Hyndman, R.J.1    Khandakar, Y.2
  • 21
    • 0036071568 scopus 로고    scopus 로고
    • A state space framework for automatic forecasting using exponential smoothing methods
    • Hyndman R.J., Koehler A.B., Snyder R.D., Grose S. A state space framework for automatic forecasting using exponential smoothing methods. Int. J. Forecast. 2002, 18(July (3)):439-454.
    • (2002) Int. J. Forecast. , vol.18 , Issue.3 JULY , pp. 439-454
    • Hyndman, R.J.1    Koehler, A.B.2    Snyder, R.D.3    Grose, S.4
  • 23
    • 84943709252 scopus 로고
    • Use of ranks in one-criterion variance analysis
    • Kruskal W.H., Wallis W.A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 1952, 47(December (260)):583.
    • (1952) J. Am. Stat. Assoc. , vol.47 , Issue.260 DECEMBER , pp. 583
    • Kruskal, W.H.1    Wallis, W.A.2
  • 24
    • 77952545391 scopus 로고    scopus 로고
    • Meta-learning for time series forecasting and forecast combination
    • Lemke C., Gabrys B. Meta-learning for time series forecasting and forecast combination. Neurocomputing 2010, 73(June (10-12)):2006-2016.
    • (2010) Neurocomputing , vol.73 , Issue.10-12 JUNE , pp. 2006-2016
    • Lemke, C.1    Gabrys, B.2
  • 27
    • 84957035400 scopus 로고    scopus 로고
    • Learning the empirical hardness of optimization problems: the case of combinatorial auctions, in: CP
    • K. Leyton-Brown, E. Nudelman, Y. Shoham, Learning the empirical hardness of optimization problems: the case of combinatorial auctions, in: CP, 2002, pp. 556-572.
    • (2002) , pp. 556-572
    • Leyton-Brown, K.1    Nudelman, E.2    Shoham, Y.3
  • 28
    • 35348987266 scopus 로고    scopus 로고
    • Empirical hardness models for combinatorial auctions
    • MIT Press, (Chapter 19), P. Cramton, Y. Shoham, R. Steinberg (Eds.)
    • Leyton-Brown K., Nudelman E., Shoham Y. Empirical hardness models for combinatorial auctions. Combinatorial Auctions 2006, 479-504. MIT Press, (Chapter 19). P. Cramton, Y. Shoham, R. Steinberg (Eds.).
    • (2006) Combinatorial Auctions , pp. 479-504
    • Leyton-Brown, K.1    Nudelman, E.2    Shoham, Y.3
  • 29
    • 68549128640 scopus 로고    scopus 로고
    • Empirical hardness models. methodology and a case study on combinatorial auctions
    • Leyton-Brown K., Nudelman E., Shoham Y. Empirical hardness models. methodology and a case study on combinatorial auctions. J. ACM 2009, 56(4):1-52.
    • (2009) J. ACM , vol.56 , Issue.4 , pp. 1-52
    • Leyton-Brown, K.1    Nudelman, E.2    Shoham, Y.3
  • 30
    • 0000270457 scopus 로고
    • New product forecasting models. directions for research and implementation
    • Mahajan V., Wind Y. New product forecasting models. directions for research and implementation. Int. J. Forecast. 1988, 4(3):341-358.
    • (1988) Int. J. Forecast. , vol.4 , Issue.3 , pp. 341-358
    • Mahajan, V.1    Wind, Y.2
  • 32
    • 0034288942 scopus 로고    scopus 로고
    • The m3-competition. results, conclusions and implications
    • Makridakis S., Hibon M. The m3-competition. results, conclusions and implications. Int. J. Forecast. 2000, 16(October (4)):451-476.
    • (2000) Int. J. Forecast. , vol.16 , Issue.4 OCTOBER , pp. 451-476
    • Makridakis, S.1    Hibon, M.2
  • 34
    • 77955849498 scopus 로고    scopus 로고
    • Practical performance models of algorithms in evolutionary program induction and other domains
    • Graff Mario, Poli Riccardo Practical performance models of algorithms in evolutionary program induction and other domains. Artif. Intell. 2010, 174(October (15)):1254-1276.
    • (2010) Artif. Intell. , vol.174 , Issue.15 OCTOBER , pp. 1254-1276
    • Graff, M.1    Poli, R.2
  • 35
    • 0011936429 scopus 로고    scopus 로고
    • Evidence for the selection of forecasting methods
    • Meade N. Evidence for the selection of forecasting methods. J. Forecast. 2000, 19(6):515-535.
    • (2000) J. Forecast. , vol.19 , Issue.6 , pp. 515-535
    • Meade, N.1
  • 36
    • 33847303915 scopus 로고    scopus 로고
    • Understanding random SAT: beyond the clauses-to-variables ratio, in: CP
    • E. Nudelman, K. Leyton-Brown, H.H. Hoos, A. Devkar, Y. Shoham, Understanding random SAT: beyond the clauses-to-variables ratio, in: CP, 2004, pp. 438-452.
    • (2004) , pp. 438-452
    • Nudelman, E.1    Leyton-Brown, K.2    Hoos, H.H.3    Devkar, A.4    Shoham, Y.5
  • 37
    • 84884204649 scopus 로고    scopus 로고
    • A field guide to genetic programming, Published via 〈〉 and freely available at 〈〉, 2008. (With contributions by J. R. Koza).
    • R. Poli, W.B. Langdon, N.F. McPhee, A field guide to genetic programming, Published via 〈〉 and freely available at 〈〉, 2008. (With contributions by J. R. Koza). http://www.gp-field-guide.org.uk.
    • Poli, R.1    Langdon, W.B.2    McPhee, N.F.3
  • 38
    • 10244243684 scopus 로고    scopus 로고
    • Meta-learning approaches to selecting time series models
    • Prudencio R.B., Ludermir T.B. Meta-learning approaches to selecting time series models. Neurocomputing 2004, 61(October (0)):121-137.
    • (2004) Neurocomputing , vol.61 , Issue.OCTOBER , pp. 121-137
    • Prudencio, R.B.1    Ludermir, T.B.2
  • 39
    • 0003056605 scopus 로고
    • The algorithm selection problem
    • Rice J.R. The algorithm selection problem. Adv. Comput. 1976, 15:65-118.
    • (1976) Adv. Comput. , vol.15 , pp. 65-118
    • Rice, J.R.1
  • 40
    • 0031537084 scopus 로고    scopus 로고
    • Model selection in univariate time series forecasting using discriminant analysis
    • Shah C. Model selection in univariate time series forecasting using discriminant analysis. Int. J. Forecast. 1997, 13(December (4)):489-500.
    • (1997) Int. J. Forecast. , vol.13 , Issue.4 DECEMBER , pp. 489-500
    • Shah, C.1
  • 41
    • 84884207649 scopus 로고    scopus 로고
    • Introduction to time series and forecasting
    • 426
    • Shanmugam R. Introduction to time series and forecasting. Technometrics 1997, 39(4). 426.
    • (1997) Technometrics , vol.39 , Issue.4
    • Shanmugam, R.1
  • 42
    • 49749086726 scopus 로고    scopus 로고
    • Cross-disciplinary perspectives on meta-learning for algorithm selection
    • Smith-Miles K.A. Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 2008, 41(1):1-25.
    • (2008) ACM Comput. Surv. , vol.41 , Issue.1 , pp. 1-25
    • Smith-Miles, K.A.1
  • 43
    • 84884209052 scopus 로고    scopus 로고
    • A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series, Working Paper 6607, National Bureau of Economic Research, June
    • J.H. Stock, M.W. Watson, A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series, Working Paper 6607, National Bureau of Economic Research, June 1998.
    • (1998)
    • Stock, J.H.1    Watson, M.W.2
  • 44
    • 67349267030 scopus 로고    scopus 로고
    • Rule induction for forecasting method selection. meta-learning the characteristics of univariate time series
    • Wang X., Smith-Miles K., Hyndman R. Rule induction for forecasting method selection. meta-learning the characteristics of univariate time series. Neurocomputing 2009, 72(June (10-12)):2581-2594.
    • (2009) Neurocomputing , vol.72 , Issue.10-12 JUNE , pp. 2581-2594
    • Wang, X.1    Smith-Miles, K.2    Hyndman, R.3
  • 45
    • 0001884644 scopus 로고
    • Individual comparisons by ranking methods
    • Wilcoxon F. Individual comparisons by ranking methods. Biometrics Bull. 1945, 1(December (6)):80.
    • (1945) Biometrics Bull. , vol.1 , Issue.6 DECEMBER , pp. 80
    • Wilcoxon, F.1
  • 46
    • 38349063502 scopus 로고    scopus 로고
    • Hierarchical hardness models for SAT, in: CP
    • L. Xu, H.H. Hoos, K. Leyton-Brown, Hierarchical hardness models for SAT, in: CP, 2007, pp. 696-711.
    • (2007) , pp. 696-711
    • Xu, L.1    Hoos, H.H.2    Leyton-Brown, K.3
  • 47
    • 38349031300 scopus 로고    scopus 로고
    • SATzilla-07: the design and analysis of an algorithm portfolio for SAT, in: CP
    • L. Xu, F. Hutter, H.H. Hoos, K. Leyton-Brown, SATzilla-07: the design and analysis of an algorithm portfolio for SAT, in: CP, 2007, pp. 712-727.
    • (2007) , pp. 712-727
    • Xu, L.1    Hutter, F.2    Hoos, H.H.3    Leyton-Brown, K.4
  • 48
    • 52249100995 scopus 로고    scopus 로고
    • SATzilla. portfolio-based algorithm selection for SAT
    • Xu L., Hutter F., Hoos H.H., Leyton-Brown K. SATzilla. portfolio-based algorithm selection for SAT. J. Artif. Intell. Res. 2008, 32(June):565-606.
    • (2008) J. Artif. Intell. Res. , vol.32 , Issue.JUNE , pp. 565-606
    • Xu, L.1    Hutter, F.2    Hoos, H.H.3    Leyton-Brown, K.4
  • 49
    • 84867879601 scopus 로고    scopus 로고
    • Model selection for RBF network via generalized degree of freedom
    • Xu P., Jayawardena A., Li W. Model selection for RBF network via generalized degree of freedom. Neurocomputing 2013, 99(January):163-171.
    • (2013) Neurocomputing , vol.99 , Issue.JANUARY , pp. 163-171
    • Xu, P.1    Jayawardena, A.2    Li, W.3


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