25 Years of Iif Time Series Forecasting: A Selective Review

We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.

[1]  A. P. Bertwistle POSITIVE OR NEGATIVE , 1955 .

[2]  Maurice Henry Quenouille,et al.  The analysis of multiple time-series , 1957 .

[3]  N. Wiener,et al.  Nonlinear Problems in Random Theory , 1964 .

[4]  M. H. Quenouille,et al.  The analysis of multiple time-series , 1958 .

[5]  Vito Volterra,et al.  Theory of Functionals and of Integral and Integro-Differential Equations , 2005 .

[6]  R. Brown Statistical forecasting for inventory control , 1960 .

[7]  R Furth,et al.  Non-Linear Problems in Random Theory , 1960 .

[8]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[9]  Peter R. Winters,et al.  Forecasting Sales by Exponentially Weighted Moving Averages , 1960 .

[10]  M. J. R. Healy,et al.  Smoothing, Forecasting and Prediction of Discrete Time Series , 1964 .

[11]  Fred C. Schweppe,et al.  Evaluation of likelihood functions for Gaussian signals , 1965, IEEE Trans. Inf. Theory.

[12]  C. B. Tilanus,et al.  Applied Economic Forecasting , 1966 .

[13]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[14]  J. D. Croston Forecasting and Stock Control for Intermittent Demands , 1972 .

[15]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[16]  R. Cox,et al.  Journal of the Royal Statistical Society B , 1972 .

[17]  M Prywes,et al.  A LOOK TO THE FUTURE , 1973, Israel journal of medical sciences.

[18]  C. Granger,et al.  Experience with Forecasting Univariate Time Series and the Combination of Forecasts , 1974 .

[19]  Paul Waltman,et al.  A Threshold Model , 1974 .

[20]  H. L. Gray,et al.  Rejoinder to comments , 1978 .

[21]  C. Granger,et al.  AN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING , 1980 .

[22]  Ronald L. Iman,et al.  Rejoinder to comments , 1980 .

[23]  Robert L. Winkler,et al.  The accuracy of extrapolation (time series) methods: Results of a forecasting competition , 1982 .

[24]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[25]  S. A. Roberts A General Class of Holt-Winters Type Forecasting Models , 1982 .

[26]  Estela Bee Dagum,et al.  Revisions of time varying seasonal filters , 1982 .

[27]  Pierre A. Cholette Prior Information and ARIMA Forecasting , 1982 .

[28]  Emanuel Parzen,et al.  ARARMA models for time series analysis and forecasting , 1982 .

[29]  Sergio G. Koreisha,et al.  Causal implications: The linkage between time series and econometric modelling , 1983 .

[30]  Paul Newbold,et al.  ARIMA model building and the time series analysis approach to forecasting , 1983 .

[31]  David M. Rocke,et al.  Municipal budget forecasting with multivariate ARMA models , 1983 .

[32]  David F. Larcker,et al.  Forecasting accounting data: A multiple time‐series analysis , 1983 .

[33]  R. Genesio,et al.  Short term load forecasting in electric power systems: A comparison of ARMA models and extended wiener filtering , 1983 .

[34]  Robert Fildes,et al.  The accuracy of extrapolation methods; an automatic box–jenkins package sift , 1984 .

[35]  Essam Mahmoud,et al.  Accuracy in forecasting: A survey , 1984 .

[36]  E. McKenzie General exponential smoothing and the equivalent arma process , 1984 .

[37]  Bovas Abraham,et al.  Some comments on the initialization of exponential smoothing , 1984 .

[38]  Robert B. Litterman Forecasting with Bayesian Vector Autoregressions-Five Years of Experience , 1984 .

[39]  Per-Olov Edlund,et al.  Identification of the multi‐input box‐Jenkins transfer function model , 1984 .

[40]  C. Granger,et al.  Improved methods of combining forecasts , 1984 .

[41]  T. Riise,et al.  Theory and practice of multivariate arma forecasting , 1984 .

[42]  Gaetan Libert,et al.  The M‐competition with a fully automatic box–jenkins procedure , 1984 .

[43]  Andrew Harvey,et al.  A unified view of statistical forecasting procedures , 1984 .

[44]  John L. Kling,et al.  A comparison of multivariate forecasting procedures for economic time series , 1985 .

[45]  Lars-Erik Öller,et al.  Macroeconomic forecasting with a vector arima model : A case study of the finnish economy , 1985 .

[46]  Everette S. Gardner,et al.  Exponential smoothing: The state of the art , 1985 .

[47]  Lars-Erik Öller,et al.  How far can changes in general business activity be forecasted , 1985 .

[48]  Arnold L. Sweet,et al.  Computing the variance of the forecast error for the holt‐winters seasonal models , 1985 .

[49]  J. David Cummins,et al.  FORECASTING AUTOMOBILE INSURANCE PAID CLAIM COSTS USING ECONOMETRIC AND ARIMA MODELS , 1985 .

[50]  Keith W. Hipel,et al.  Forecasting monthly riverflow time series , 1985 .

[51]  D. Bunn,et al.  Statistical efficiency in the linear combination of forecasts , 1985 .

[52]  Donald Poskitt,et al.  The selection and use of linear and bilinear time series models , 1986 .

[53]  Allan P. Layton,et al.  An international comparison of economic leading indicators of telecommunications traffic , 1986 .

[54]  E. McKenzie,et al.  Error analysis for winters' additive seasonal forecasting system , 1986 .

[55]  F. R. Johnston,et al.  The Variance of Lead-Time Demand , 1986 .

[56]  Pierre A. Cholette,et al.  Mutivariate ARIMA forecasting of irregular time series , 1986 .

[57]  Helmut Lütkepohl,et al.  Comparison of predictors for temporally and contemporaneously aggregated time series , 1986 .

[58]  Robert M. Kunst,et al.  A forecasting comparison of some var techniques , 1986 .

[59]  G. Huyot,et al.  Analysis of Revisions in the Seasonal Adjustment of Data Using X-11-Arima Model-Based filters , 1986 .

[60]  Michael D. Geurts,et al.  Forecasting retail sales using alternative models , 1986 .

[61]  Robert Pavur,et al.  A comparison of the accuracy of the Box-Jenkins method with that of automated forecasting methods , 1987 .

[62]  Rik Hafer,et al.  The sensitivity of VAR forecasts to alternative lag structures , 1987 .

[63]  Satya Dev Gupta,et al.  Testing causality: Some caveats and a suggestion , 1987 .

[64]  GuptaSunil,et al.  Combination of Forecasts , 1987 .

[65]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[66]  Francis X. Diebold,et al.  The use of prior information in forecast combination , 1990 .

[67]  Spyros Makridakis,et al.  Confidence intervals: An empirical investigation of the series in the M-competition , 1987 .

[68]  Robert P. Leone Forecasting the effect of an environmental change on market performance: An intervention time-series approach , 1987 .

[69]  Phoebus J. Dhrymes,et al.  A comparison of the forecasting performance of WEFA and ARIMA time series methods , 1988 .

[70]  C. Chatfield,et al.  Apples, oranges and mean square error , 1988 .

[71]  Scott E. Hein,et al.  Forecasting the daily federal funds rate , 1988 .

[72]  Everette S. Gardner,et al.  Model Identification in Exponential Smoothing , 1988 .

[73]  J. Keith Ord,et al.  Future developments in forecasting : The time series connexion , 1988 .

[74]  R. Fildes,et al.  Forecasting and loss functions , 1988 .

[75]  R.M.J. Heuts,et al.  Forecasting the Dutch heavy truck market : A multivariate approach , 1988 .

[76]  Chris Chatfield,et al.  The future of time-series forecasting , 1988 .

[77]  Kenneth O. Cogger,et al.  Proposals for research in time series forecasting , 1988 .

[78]  James R. Wilson,et al.  Pitfalls in simulation-based evaluation of forecast monitoring schemes , 1988 .

[79]  Richard A. Ashley,et al.  On the relative worth of recent macroeconomic forecasts , 1988 .

[80]  John O. McClain,et al.  Dominant tracking signals , 1988 .

[81]  E. S. Gardner,et al.  Seasonal exponential smoothing with damped trends , 1989 .

[82]  Paul Newbold,et al.  On exponential smoothing and the assumption of deterministic trend plus white noise data-generating models , 1989 .

[83]  Arnold L. Sweet,et al.  A note on a comparison of exponential smoothing methods for forecasting seasonal series , 1989 .

[84]  J. Ledolter The effect of additive outliers on the forecasts from ARIMA models , 1989 .

[85]  Benito E. Flores,et al.  The utilization of the Wilcoxon test to compare forecasting methods: A note , 1989 .

[86]  Lakshman Krishnamurthi,et al.  Intervention analysis using control series and exogenous variables in a transfer function model: A case study , 1989 .

[87]  Danny Pfeffermann,et al.  Multivariate exponential smoothing: Method and practice , 1989 .

[88]  Winston T. Lin Modeling and forecasting hospital patient movements: Univariate and multiple time series approaches , 1989 .

[89]  Richard Withycombe,et al.  Forecasting with combined seasonal indices , 1989 .

[90]  Pierre Lefrançois,et al.  Confidence intervals for non-stationary forecast errors: Some empirical results for the series in the M-competition , 1989 .

[91]  J. Keith Ord,et al.  Forecasting using automatic identification procedures: A comparative analysis , 1989 .

[92]  Barry R. Weller National indicator series as quantitative predictors of small region monthly employment levels , 1989 .

[93]  J. LeSage Incorporating regional wage relations in local forecasting models with a Bayesian prior , 1989 .

[94]  A comparison of quarterly earnings per share forecasts using James-Stein and unconditional least squares parameter estimators , 1989 .

[95]  R. Clemen Combining forecasts: A review and annotated bibliography , 1989 .

[96]  W. D. Ray Rates of convergence to steady state for the linear growth version of a dynamic linear model (DLM) , 1989 .

[97]  Michael J. Artis,et al.  BVAR forecasts for the G-7 , 1990 .

[98]  Guido Masarotto,et al.  Bootstrap prediction intervals for autoregressions , 1990 .

[99]  D. J. Pack Comments on: "In defense of ARIMA modeling", by M.D. Geurts and J.P. Kelly , 1990 .

[100]  Patrick A. Thompson,et al.  An MSE statistic for comparing forecast accuracy across series , 1990 .

[101]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[102]  Irma J. Terpenning,et al.  STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .

[103]  LeRoy F. Simmons,et al.  Time-series decomposition using the sinusoidal model , 1990 .

[104]  Stefan Mittnik,et al.  Macroeconomic forecasting experience with balanced state space models , 1990 .

[105]  Ken Holden,et al.  An examination of vector autoregressive forecasts for the U.K. economy , 1990 .

[106]  Blyth C. Archibald Parameter space of the Holt-Winters' model , 1990 .

[107]  Ralph D. Snyder,et al.  Structural time series models in inventory control , 1990 .

[108]  David J. Pack,et al.  In defense of ARIMA modeling , 1990 .

[109]  Michael Funke,et al.  Assessing the forecasting accuracy of monthly vector autoregressive models: The case of five OECD countries , 1990 .

[110]  Werner A. Stahel,et al.  Forecasting demand for special telephone services: A case study , 1990 .

[111]  Michael D. Geurts,et al.  “In defense of ARIMA modeling”, by D.J. Pack , 1990 .

[112]  C. Chatfield,et al.  Prediction intervals for the Holt-Winters forecasting procedure , 1990 .

[113]  Jose Juan Carreno,et al.  A modification of time series forecasting methods for handling announced price increases , 1990 .

[114]  Anne B. Koehler,et al.  An inappropriate prediction interval , 1990 .

[115]  J. G. D. Gooijer The role of time series analysis in forecasting: A personal view , 1990 .

[116]  Denise R. Osborn,et al.  A survey of seasonality in UK macroeconomic variables , 1990 .

[117]  Michèle Hibon,et al.  Exponential smoothing: The effect of initial values and loss functions on post-sample forecasting accuracy , 1991 .

[118]  Leonard J. Tashman,et al.  Automatic forecasting software: A survey and evaluation☆ , 1991 .

[119]  Víctor M. Guerrero ARIMA forecasts with restrictions derived from a structural change , 1991 .

[120]  Lon-Mu Liu,et al.  Forecasting residential consumption of natural gas using monthly and quarterly time series , 1991 .

[121]  Anne B. Koehler,et al.  On confusing lead time demand with h-period-ahead forecasts , 1991 .

[122]  Wen Lea Pearn,et al.  Assessing the statistical characteristics of the mean absolute error or forecasting , 1991 .

[123]  James P. LeSage,et al.  Using interindustry input-output relations as a Bayesian prior in employment forecasting models , 1991 .

[124]  Patrick A. Thompson,et al.  Evaluation of the M-competition forecasts via log mean squared error ratio , 1991 .

[125]  J. Keith Ord,et al.  Automatic forecasting using explanatory variables: A comparative study , 1991 .

[126]  C. Chatfield,et al.  Prediction intervals for multiplicative Holt-Winters , 1991 .

[127]  Robert Fildes,et al.  The evaluation of extrapolative forecasting methods , 1992 .

[128]  Dennis A. Ahlburg,et al.  A Commentary on Error Measures , 1992 .

[129]  P Pflaumer,et al.  Forecasting U.S. population totals with the Box-Jenkins approach. , 1992, International journal of forecasting.

[130]  Gary L. Shoesmith Non-cointegration and causality: Implications for VAR modeling , 1992 .

[131]  Chris Chatfield,et al.  A commentary on error measures , 1992 .

[132]  Fred Collopy,et al.  Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations , 1992 .

[133]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[134]  Rob J. Hyndman,et al.  On continuous-time threshold autoregression☆ , 1992 .

[135]  Paul A. Coomes,et al.  A Kalman filter formulation for noisy regional job data , 1992 .

[136]  Jan G. De Gooijer,et al.  On the cumulated multi-step-ahead predictions of vector autoregressive moving average processes , 1992 .

[137]  Sevket I. Gunter Nonnegativity restricted least squares combinations , 1992 .

[138]  Robert L. Winkler,et al.  The effect of nonstationarity on combined forecasts , 1992 .

[139]  J. Gooijer,et al.  Some recent developments in non-linear time series modelling, testing, and forecasting☆ , 1992 .

[140]  Sevket I. Gunter,et al.  An empirical analysis of the accuracy of SA, OLS, ERLS and NRLS combination forecasts , 1992 .

[141]  R. Chou,et al.  ARCH modeling in finance: A review of the theory and empirical evidence , 1992 .

[142]  Spyros Makridakis,et al.  Accuracy measures: theoretical and practical concerns☆ , 1993 .

[143]  C. Chatfield,et al.  Neural networks: Forecasting breakthrough or passing fad? , 1993 .

[144]  Luiz Koodi Hotta,et al.  The effect of additive outliers on the estimates from aggregated and disaggregated ARIMA models , 1993 .

[145]  B. Ray Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model , 1993 .

[146]  Enrique Alba,et al.  Constrained forecasting in autoregressive time series models: A Bayesian analysis , 1993 .

[147]  Sune Karlsson,et al.  Forecasting the Swedish unemployment rate VAR vs. transfer function modelling , 1993 .

[148]  Matthew J. Liberatore,et al.  Seasonal exponential smoothing with damped trends: An application for production planning , 1993 .

[149]  D. Bunn,et al.  Using group seasonal indices in multi-item short-term forecasting , 1993 .

[150]  David E. Spencer Developing a Bayesian vector autoregression forecasting model , 1993 .

[151]  Lon-Mu Liu,et al.  Dynamic structural analysis and forecasting of residential electricity consumption , 1993 .

[152]  C. Granger,et al.  Modelling Nonlinear Economic Relationships , 1995 .

[153]  Philip Hans Franses,et al.  Periodic integration in quarterly UK macroeconomic variables , 1993 .

[154]  Everette S. Gardner,et al.  Forecasting the failure of component parts in computer systems: A case study , 1993 .

[155]  Scott H. Irwin,et al.  The performance of alternative VAR models in forecasting exchange rates , 1994 .

[156]  Robyn M. Dawes,et al.  The past and the future of forecasting research , 1994 .

[157]  Jeremy P. Smith,et al.  Forecasting costs incurred from unit differencing fractionally integrated processes , 1994 .

[158]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[159]  Víctor M. Guerrero,et al.  Restricted forecasts using exponential smoothing techniques , 1994 .

[160]  T. Willemain,et al.  Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method , 1994 .

[161]  Wilpen L. Gorr,et al.  Comparative study of artificial neural network and statistical models for predicting student grade point averages , 1994 .

[162]  Marcus O'Connor,et al.  Artificial neural network models for forecasting and decision making , 1994 .

[163]  Paul Newbold,et al.  Adventures with ARIMA software , 1994 .

[164]  Wilpen L. Gorr,et al.  Editorial: Research prospective on neural network forecasting , 1994 .

[165]  A. Tegene,et al.  Evaluating forecasting models of farmland prices , 1994 .

[166]  Allan Timmermann,et al.  On the optimality of adaptive expectations: Muth revisited , 1995 .

[167]  G. L. Shoesmith,et al.  Multiple cointegrating vectors, error correction, and forecasting with Litterman's model , 1995 .

[168]  H. Vinod,et al.  Forecasting consumption, income and real interest rates from alternative state space models , 1995 .

[169]  Rob J. Hyndman,et al.  Highest‐density forecast regions for nonlinear and non‐normal time series models , 1995 .

[170]  Kerry Patterson,et al.  Forecasting the final vintage of real personal disposable income: A state space approach , 1995 .

[171]  Scott P. Simkins Forecasting with vector autoregressive (VAR) models subject to business cycle restrictions , 1995 .

[172]  Danny Pfeffermann,et al.  Estimation of the variances of X-11 ARIMA seasonally adjusted estimators for a multiplicative decomposition and heteroscedastic variances , 1995 .

[173]  Kurt Brfinnfis Prediction and control for a time-series count data model , 1995 .

[174]  Stephen P. Curram,et al.  Forecasting consumers' expenditure: A comparison between econometric and neural network models , 1996 .

[175]  J. E. Boylan,et al.  Forecasting intermittent demand: A comparative evaluation of croston's method. Comment , 1996 .

[176]  Michael P. Clements,et al.  The Performance of Alternative Forecasting Methods for SETAR Models , 1997 .

[177]  P. Franses,et al.  Forecasting the Levels of Vector Autoregressive Log-Transformed Time Series , 1996 .

[178]  Jonathan Aylen,et al.  Modelling the Great Lakes Freeze: forecasting and seasonality in the market for ferrous scrap , 1996 .

[179]  Leonard J. Tashman,et al.  The use of protocols to select exponential smoothing procedures: A reconsideration of forecasting competitions , 1996 .

[180]  Yufei Yuan,et al.  Neural network forecasting of quarterly accounting earnings , 1996 .

[181]  Cahit Erkal,et al.  Distinguishing between stochastic and deterministic behavior in high frequency foreign exchange rate returns: Can non-linear dynamics help forecasting?☆ , 1996 .

[182]  Hans-Eggert Reimers,et al.  Forecasting of seasonal cointegrated processes , 1997 .

[183]  Francesco Lisi,et al.  Is a random walk the best exchange rate predictor , 1997 .

[184]  Michael P. Clements,et al.  An empirical study of seasonal unit roots in forecasting , 1997 .

[185]  W. E. Watt,et al.  Nested threshold autoregressive (NeTAR) models , 1997 .

[186]  J. Ord,et al.  Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models , 1997 .

[187]  Phoebus J. Dhrymes,et al.  Structural VAR, Marma and Open Economy Models , 1997 .

[188]  Norman R. Swanson,et al.  Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models , 1997 .

[189]  Maxwell L. King,et al.  Forecasting international quarterly tourist flows using error-correction and time-series models , 1997 .

[190]  A. M. Robert Taylor,et al.  On the practical problems of computing seasonal unit root tests , 1997 .

[191]  Alfonso Novales,et al.  Forecasting with periodic models A comparison with time invariant coefficient models , 1997 .

[192]  Paul Newbold,et al.  Testing the equality of prediction mean squared errors , 1997 .

[193]  Philip Hans Franses,et al.  Mean Shifts, Unit Roots and Forecasting Seasonal Time Series , 1997 .

[194]  Mariano J. Valderrama,et al.  A principal component approach to dynamic regression models , 1997 .

[195]  Adrian Pagan,et al.  Seasonal Integration and the Evolving Seasonals Model , 1996 .

[196]  Philip Hans Franses,et al.  A periodic long memory model for quarterly UK inflation , 1997 .

[197]  J. M. Wells Modelling seasonal patterns and long-run trends in U.S. time series , 1997 .

[198]  Chunhang Chen,et al.  Robustness properties of some forecasting methods for seasonal time series: A Monte Carlo study☆ , 1997 .

[199]  Helmut Herwartz,et al.  Performance of periodic error correction models in forecasting consumption data , 1997 .

[200]  A. Fiordaliso A nonlinear forecasts combination method based on Takagi–Sugeno fuzzy systems , 1998 .

[201]  Jeffrey E. Jarrett,et al.  Improving forecasting for telemarketing centers by ARIMA modeling with intervention , 1998 .

[202]  Robert Fildes,et al.  Generalising about univariate forecasting methods: Further empirical evidence , 1998 .

[203]  Matteo Grigoletto Bootstrap prediction intervals for autoregressive models fitted to non-autoregressive processes , 1998 .

[204]  J. Stock,et al.  A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series , 1998 .

[205]  Anthony S. Tay,et al.  Evaluating Density Forecasts with Applications to Financial Risk Management , 1998 .

[206]  Matteo Grigoletto,et al.  Bootstrap prediction intervals for autoregressions: some alternatives , 1998 .

[207]  Philip Hans Franses,et al.  A model selection strategy for time series with increasing seasonal variation , 1998 .

[208]  Víctor Gómez,et al.  Seasonal Adjustment and Signal Extraction in Economic Time Series , 1998 .

[209]  Prasad V. Bidarkota The comparative forecast performance of univariate and multivariate models: an application to real interest rate forecasting , 1998 .

[210]  Walter Enders,et al.  Threshold-autoregressive, median-unbiased, and cointegration tests of purchasing power parity , 1998 .

[211]  T. Andersen THE ECONOMETRICS OF FINANCIAL MARKETS , 1998, Econometric Theory.

[212]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[213]  R. Lawton How should additive Holt–Winters estimates be corrected? , 1998 .

[214]  Peter E. Kennedy,et al.  Combining Qualitative Forecasts Using Logit , 1998 .

[215]  Derek W. Bunn,et al.  The persistence of specification problems in the distribution of combined forecast errors , 1998 .

[216]  P. Goodwin,et al.  On the asymmetry of the symmetric MAPE , 1999 .

[217]  Derek W. Bunn,et al.  Investigating improvements in the accuracy of prediction intervals for combinations of forecasts: A simulation study , 1999 .

[218]  R. Engle,et al.  CAViaR , 1999 .

[219]  P. Franses,et al.  Additive outliers, GARCH and forecasting volatility , 1999 .

[220]  Juan Romo,et al.  Effects of parameter estimation on prediction densities: a bootstrap approach , 1999 .

[221]  K. Wallis Asymmetric density forecasts of inflation and the Bank of England's fan chart , 1999, National Institute Economic Review.

[222]  Kurt Brännäs,et al.  A New Approach to Modelling and Forecasting Monthly Guest Nights in Hotels , 2001 .

[223]  Anne B. Koehler,et al.  Forecasting models and prediction intervals for the multiplicative Holt-Winters method , 2001 .

[224]  Daniel W. Williams,et al.  Level-adjusted exponential smoothing for modeling planned discontinuities1 , 1999 .

[225]  Abdol S. Soofi,et al.  Nonlinear deterministic forecasting of daily dollar exchange rates , 1999 .

[226]  Jae H. Kim Asymptotic and bootstrap prediction regions for vector autoregression , 1999 .

[227]  James D. Hamilton A Parametric Approach to Flexible Nonlinear Inference , 2001 .

[228]  Derek W. Bunn,et al.  Comparison of seasonal estimation methods in multi-item short-term forecasting , 1999 .

[229]  Julián Andrada-Félix,et al.  Exchange-rate forecasts with simultaneous nearest-neighbour methods: evidence from the EMS , 1999 .

[230]  H. V. Dijk,et al.  Combined forecasts from linear and nonlinear time series models , 1999 .

[231]  Abdol S. Soofi,et al.  Nonlinear deterministic forecasting of daily Peseta–Dollar exchange rate , 1999 .

[232]  Walter Enders,et al.  Estimating non-linear ARMA models using Fourier coefficients , 2000 .

[233]  Clifford M. Hurvich,et al.  Multistep Forecasting of Long Memory Series Using Fractional Exponential Models , 2002 .

[234]  Tommaso Proietti,et al.  Comparing seasonal components for structural time series models , 2000 .

[235]  K. Nikolopoulos,et al.  The theta model: a decomposition approach to forecasting , 2000 .

[236]  Qiwei Yao,et al.  Conditional Minimum Volume Predictive Regions for Stochastic Processes , 2000 .

[237]  Kenneth F. Wallis,et al.  Density Forecasting: A Survey , 2000 .

[238]  Guy Melard,et al.  Automatic ARIMA modeling including interventions, using time series expert software , 2000 .

[239]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[240]  Nigel Meade,et al.  A note on the Robust Trend and ARARMA methodologies used in the M3 Competition , 2000 .

[241]  J. Keith Ord,et al.  Automatic neural network modeling for univariate time series , 2000 .

[242]  Jan G. De Gooijer,et al.  Nonparametric conditional predictive regions for time series , 2000 .

[243]  Rob J Hyndman,et al.  Theory & Methods: Non‐Gaussian Conditional Linear AR(1) Models , 2000 .

[244]  Leonard J. Tashman,et al.  Out-of-sample tests of forecasting accuracy: an analysis and review , 2000 .

[245]  Georges A. Darbellay,et al.  Forecasting the short-term demand for electricity: Do neural networks stand a better chance? , 2000 .

[246]  Michael K. Andersson Do long-memory models have long memory? , 2000 .

[247]  J. Ord,et al.  A New Look at Models For Exponential Smoothing , 2001 .

[248]  Helmut Herwartz,et al.  Investigating the JPY/DEM-rate: arbitrage opportunities and a case for asymmetry , 2001 .

[249]  Michael P. Clements,et al.  Bootstrapping prediction intervals for autoregressive models , 2001 .

[250]  Kenneth F. Wallis,et al.  Chi-Squared Tests of Interval and Density Forecasts, and the Bank of England's Fan Charts , 2001, SSRN Electronic Journal.

[251]  Chris Brooks,et al.  Benchmarks and the accuracy of GARCH model estimation , 2001 .

[252]  Philip Hans Franses,et al.  The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production , 2001 .

[253]  Nicholas Sarantis,et al.  Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence , 2001 .

[254]  M. Qi Predicting US recessions with leading indicators via neural network models , 2001 .

[255]  Michael P. Clements,et al.  Pooling of Forecasts , 2004 .

[256]  Timo Teräsvirta,et al.  The combination of forecasts using changing weights , 1994 .

[257]  Greg Tkacz Neural network forecasting of Canadian GDP growth , 2001 .

[258]  Ryan Sullivan,et al.  Forecast Evaluation with Shared Data Sets , 2001 .

[259]  Rob J. Hyndman,et al.  It's time to move from 'what' to 'why' , 2001 .

[260]  Mattias Villani,et al.  Bayesian prediction with cointegrated vector autoregressions , 2001 .

[261]  Rob J Hyndman,et al.  Prediction Intervals for Exponential Smoothing State Space Models , 2001 .

[262]  Howard Grubb,et al.  Long lead-time forecasting of UK air passengers by Holt-Winters methods with damped trend , 2001 .

[263]  Michael P. Clements,et al.  Evaluating multivariate forecast densities: a comparison of two approaches , 2002 .

[264]  Piotr Kokoszka,et al.  Computation of the forecast coefficients for multistep prediction of long-range dependent time series , 2002 .

[265]  Jan Beran,et al.  On robust local polynomial estimation with long-memory errors , 2002 .

[266]  Massimiliano Giuseppe Marcellino,et al.  Forecasting Emu Macroeconomic Variables , 2002 .

[267]  Bias in the memory parameter for different sampling rates , 2002 .

[268]  R. Baillie,et al.  Modeling and forecasting from trend-stationary long memory models with applications to climatology , 2002 .

[269]  Rob J Hyndman,et al.  A state space framework for automatic forecasting using exponential smoothing methods , 2002 .

[270]  M. Veall,et al.  Bootstrap prediction intervals for single period regression forecasts , 2002 .

[271]  Nalini Ravishanker,et al.  Bayesian prediction for vector ARFIMA processes , 2002 .

[272]  Nigel Meade,et al.  A comparison of the accuracy of short term foreign exchange forecasting methods , 2002 .

[273]  Jae H. Kim Forecasting autoregressive time series with bias-corrected parameter estimators , 2003 .

[274]  Daniel W. Williams,et al.  Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy , 2003 .

[275]  In-Bong Kang,et al.  Multi-period forecasting using different models for different horizons: an application to U.S. economic time series data , 2003 .

[276]  James W. Taylor Exponential smoothing with a damped multiplicative trend , 2003 .

[277]  Donald Poskitt,et al.  On the specification of cointegrated autoregressive moving-average forecasting systems , 2003 .

[278]  Rob J Hyndman,et al.  Unmasking the Theta Method , 2003 .

[279]  Emanuela Marrocu,et al.  THE PERFORMANCE OF SETAR MODELS: A REGIME CONDITIONAL EVALUATION OF POINT, INTERVAL AND DENSITY FORECASTS , 2004 .

[280]  Benoit Quenneville,et al.  A note on Musgrave asymmetrical trend-cycle filters , 2003 .

[281]  Qing-Guo Wang,et al.  Transfer Function Modelling , 2003 .

[282]  S. F. Witt,et al.  Univariate versus multivariate time series forecasting: an application to international tourism demand , 2003 .

[283]  Anne B. Koehler,et al.  Normalization of seasonal factors in Winters’ methods , 2003 .

[284]  Dennis Olson,et al.  Neural network forecasts of Canadian stock returns using accounting ratios , 2003 .

[285]  K. Man,et al.  Long memory time series and short term forecasts , 2003 .

[286]  Yue Fang,et al.  Forecasting combination and encompassing tests , 2003 .

[287]  Francisco F. R. Ramos Forecasts of market shares from VAR and BVAR models: a comparison of their accuracy , 2003 .

[288]  Comparing forecasts of inflation using time distance , 2003 .

[289]  Yongil Jeon,et al.  A time-distance criterion for evaluating forecasting models , 2003 .

[290]  O. Linton,et al.  A GARCH model of the implied volatility of the Swiss Market Index from options prices , 2004 .

[291]  Brendan McCabe,et al.  Forecasting discrete valued low count time series , 2004 .

[292]  Hui Zou,et al.  Combining time series models for forecasting , 2004, International Journal of Forecasting.

[293]  Daniel W. Williams,et al.  Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program , 2004 .

[294]  Jan G. De Gooijer,et al.  Forecasting threshold cointegrated systems , 2004 .

[295]  Jae H. Kim Bootstrap prediction intervals for autoregression using asymptotically mean-unbiased estimators , 2004 .

[296]  Parameter estimation and tests of equal forecast accuracy between non-nested models , 2004 .

[297]  Michael P. Clements,et al.  Forecasting economic and financial time-series with non-linear models , 2004 .

[298]  Keith Ord,et al.  Shrinking: When and how? , 2004 .

[299]  T. Willemain,et al.  A new approach to forecasting intermittent demand for service parts inventories , 2004 .

[300]  Michael D. Bradley,et al.  Forecasting with a nonlinear dynamic model of stock returns and industrial production , 2004 .

[301]  C. Holt Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .

[302]  Jeremy P. Smith,et al.  Effects of temporal aggregation on estimates and forecasts of fractionally integrated processes: a Monte-Carlo study , 2004 .

[303]  Philip Hans Franses,et al.  Forecasting unemployment using an autoregression with censored latent effects parameters , 2004 .

[304]  Benoit Quenneville,et al.  Implementation issues on shrinkage estimators for seasonal factors within the X-11 seasonal adjustment method , 2004 .

[305]  J. Geweke,et al.  Bayesian Forecasting , 2004 .

[306]  David A. Bessler,et al.  Forecasting performance of multivariate time series models with full and reduced rank: An empirical examination , 2004 .

[307]  Rob J. Hyndman,et al.  The interaction between trend and seasonality , 2004 .

[308]  Christian M. Dahl,et al.  Flexible regression models and relative forecast performance , 2004 .

[309]  Charles C. Holt,et al.  Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .

[310]  David F. Hendry,et al.  Non-Parametric Direct Multi-Step Estimation for Forecasting Economic Processes , 2004 .

[311]  David F. Findley,et al.  Seasonal adjustment perspectives on “Damping seasonal factors: shrinkage estimators for the X-12-ARIMA program” , 2004 .

[312]  Saeed Heravi,et al.  Linear versus neural network forecasts for European industrial production series , 2004 .

[313]  Anne B. Koehler,et al.  Comments on damped seasonal factors and decisions by potential users , 2004 .

[314]  David Zimbra,et al.  A dynamic artificial neural network model for forecasting time series events , 2005 .

[315]  Melvin J. Hinich,et al.  Time Series Analysis by State Space Methods , 2001 .

[316]  T. Evgeniou,et al.  To combine or not to combine: selecting among forecasts and their combinations , 2005 .

[317]  John W. Galbraith,et al.  Content horizons for conditional variance forecasts , 2005 .

[318]  J. Boylan,et al.  The accuracy of intermittent demand estimates , 2005 .

[319]  Valentina Corradi,et al.  Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries , 2005 .

[320]  Jesper Hansson,et al.  Business survey data: Do they help in forecasting GDP growth? , 2005 .

[321]  Gael M. Martin,et al.  Bayesian predictions of low count time series , 2005 .

[322]  J. Wieringa,et al.  Computing level-impulse responses of log-specified VAR systems , 2005 .

[323]  Rob J Hyndman,et al.  Prediction intervals for exponential smoothing using two new classes of state space models 30 January 2003 , 2003 .

[324]  Juan Romo,et al.  Bootstrap prediction intervals for power-transformed time series , 2005 .

[325]  William B. White,et al.  All in the Family , 2005 .

[326]  J. J. Reeves Bootstrap prediction intervals for ARCH models , 2005 .

[327]  R. Koenker,et al.  Regression Quantiles , 2007 .