-
1
-
-
0034288943
-
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
-
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
-
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
-
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
-
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
-
-
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
-
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
-
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
-
12
-
-
0003828513
-
-
Oxford University Press, Oxford
-
Durbin J., Koopman S.J., Atkinson A.C. Time Series Analysis by State Space Methods, vol. 15 2001, Oxford University Press, Oxford.
-
(2001)
Time Series Analysis by State Space Methods, vol. 15
-
-
Durbin, J.1
Koopman, S.J.2
Atkinson, A.C.3
-
13
-
-
3242708140
-
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
-
-
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
-
16
-
-
0003708669
-
-
Prentice Hall
-
Holt C.C., Modigliani F., Muth J.F., Simon H.A., Bonini C.P., Winters P.R. Planning Production, Inventories, and Work Force 1960, Prentice Hall.
-
(1960)
Planning Production, Inventories, and Work Force
-
-
Holt, C.C.1
Modigliani, F.2
Muth, J.F.3
Simon, H.A.4
Bonini, C.P.5
Winters, P.R.6
-
17
-
-
33750380289
-
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
-
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
-
-
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
-
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
-
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
-
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
-
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
-
25
-
-
33244494375
-
-
Boosting as a metaphor for algorithm design, in: CP
-
K. Leyton-Brown, E. Nudelman, G. Andrew, J. McFadden, Y. Shoham, Boosting as a metaphor for algorithm design, in: CP, 2003, pp. 899-903.
-
(2003)
, pp. 899-903
-
-
Leyton-Brown, K.1
Nudelman, E.2
Andrew, G.3
McFadden, J.4
Shoham, Y.5
-
26
-
-
84880774150
-
-
A portfolio approach to algorithm selection, in: IJCAI
-
K. Leyton-Brown, E. Nudelman, G. Andrew, J. McFadden, Y. Shoham, A portfolio approach to algorithm selection, in: IJCAI, 2003, pp. 1542.
-
(2003)
, pp. 1542
-
-
Leyton-Brown, K.1
Nudelman, E.2
Andrew, G.3
McFadden, J.4
Shoham, Y.5
-
27
-
-
84957035400
-
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
-
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
-
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
-
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
-
31
-
-
84984426556
-
The accuracy of extrapolation (time series) methods. results of a forecasting competition
-
Makridakis S., Andersen A., Carbone R., Fildes R., Hibon M., Lewandowski R., Newton J., Parzen E., Winkler R. The accuracy of extrapolation (time series) methods. results of a forecasting competition. J. Forecast. 1982, 1(2):111-153.
-
(1982)
J. Forecast.
, vol.1
, Issue.2
, pp. 111-153
-
-
Makridakis, S.1
Andersen, A.2
Carbone, R.3
Fildes, R.4
Hibon, M.5
Lewandowski, R.6
Newton, J.7
Parzen, E.8
Winkler, R.9
-
32
-
-
0034288942
-
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
-
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
-
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
-
-
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
-
-
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
-
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
-
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
-
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
-
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
-
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
-
-
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
-
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
-
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
-
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
-
-
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
-
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
-
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
|