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Meta‐learning how to forecast time series
15 references·3 citing·35 second hop
Meta‐learning how to forecast time series (2023)Meta‐learning how to forecast time seriesThe M4 Competition: 100,000 time series and 61 forecasting methods (2019)The M4 Competition: 100,000 t…FFORMA: Feature-based forecast model averaging (2019)FFORMA: Feature-based forecas…Visualising forecasting algorithm performance using time series instance spaces (2017)Visualising forecasting algor…Automatic time series forecasting: the forecast package for R (2017)Automatic time series forecas…Meta-learning for time series forecasting and forecast combination (2010)Meta-learning for time series…Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series (2008)Rule induction for forecastin…Forecasting with Exponential Smoothing (2008)Forecasting with Exponential …Rule-based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations (2007)Rule-based Forecasting: Devel…Meta-learning approaches to selecting time series models (2004)Meta-learning approaches to s…A state space framework for automatic forecasting using exponential smoothing methods (2002)A state space framework for a…The M3-Competition: results, conclusions and implications (2000)The M3-Competition: results, …Time‐series analysis supported by power transformations (1993)Time‐series analysis supporte…Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource (1989)Space-Time Modelling with Lon…The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)The accuracy of extrapolation…Forecasting principles and practiceForecasting principles and pr…Pattern recognition in financial surveillance with the ARMA-GARCH time series model using support vector machine (2021)Pattern recognition in financ…Investigating the accuracy of cross-learning time series forecasting methods (2020)Investigating the accuracy of…Fast and accurate yearly time series forecasting with forecast combinations (2019)Fast and accurate yearly time…Combining forecasts: A review and annotated bibliography (1989)25 years of time series forecasting (2006)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)The Combination of Forecasts (1969)Time Series Analysis: Forecasting and Control (1977)Exponential smoothing: The state of the art (1985)Error measures for generalizing about forecasting methods: Empirical comparisons (1992)Forecasting methods and applications (1993)Spatial statistics (1981)Statistical Methods for Data with Long-Range Dependence (1992)Geostatistical Space–Time Models: A Review (1999)Space–Time Covariance Functions (2005)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Criteria for classifying forecasting methods (2019)Kaggle forecasting competitions: An overlooked learning opportunity (2020)Forecast combinations: An over 50-year review (2022)Time Series Analysis Forecasting and Control. (1971)Another look at measures of forecast accuracy (2006)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Forecast evaluation for data scientists: common pitfalls and best practices (2022)Forecasting: theory and practice (2022)M5 accuracy competition: Results, findings, and conclusions (2022)Protocol Analysis: Verbal Reports as Data (1986)Combining forecasts: A review and annotated bibliography (1990)A prototype expert system for the evaluation and selection of potential suppliers (1996)Review of guidelines for the use of combined forecasts (2000)Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011)A new metric of absolute percentage error for intermittent demand forecasts (2016)Forecasting and operational research: a review (2008)Forecasting with temporal hierarchies (2017)Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2022)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)The tourism forecasting competition (2010)
this work references cited by second hop

References

  • n.d.Forecasting principles and practice4,364 cited
  • 2000The M3-Competition: results, conclusions and implications1,700 cited
  • 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition1,465 cited
  • 2002A state space framework for automatic forecasting using exponential smoothing methods1,074 cited
  • 2008Forecasting with Exponential Smoothing1,040 cited
  • 2019The M4 Competition: 100,000 time series and 61 forecasting methods990 cited
  • 2017Automatic time series forecasting: the forecast package for R978 cited
  • 1989Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource625 cited
  • 2019FFORMA: Feature-based forecast model averaging313 cited
  • 2007Rule-based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations277 cited
  • 2010Meta-learning for time series forecasting and forecast combination244 cited
  • 2008Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series176 cited
  • 2017Visualising forecasting algorithm performance using time series instance spaces157 cited
  • 2004Meta-learning approaches to selecting time series models147 cited

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1993
Time‐series analysis supported by power transformations
146 cited

Cited by

  • 2020Investigating the accuracy of cross-learning time series forecasting methods78 cited
  • 2019Fast and accurate yearly time series forecasting with forecast combinations51 cited
  • 2021Pattern recognition in financial surveillance with the ARMA-GARCH time series model using support vector machine29 cited