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Unmasking the Theta method
8 references·16 citing·31 second hop
Unmasking the Theta method (2002)Unmasking the Theta methodA state space framework for automatic forecasting using exponential smoothing methods (2002)A state space framework for a…The theta model: a decomposition approach to forecasting (2000)The theta model: a decomposit…The M3-Competition: results, conclusions and implications (2000)The M3-Competition: results, …Forecasting: Methods and Applications (1999)Forecasting: Methods and Appl…Forecasting methods and applications (1993)Forecasting methods and appli…Time Series Analysis: Forecasting and Control. (1978)Time Series Analysis: Forecas…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecas…Time Series Analysis, Forecasting and Control. (1971)Time Series Analysis, Forecas…STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones (2021)STL Decomposition of Time Ser…Reinforcement learning framework for freight demand forecasting to support operational planning decisions (2020)Reinforcement learning framew…Real-time demand forecasting for an urban delivery platform (2020)Real-time demand forecasting …Generalizing the Theta method for automatic forecasting (2020)Generalizing the Theta method…A brief history of forecasting competitions (2019)A brief history of forecastin…The inventory performance of forecasting methods: Evidence from the M3 competition data (2018)The inventory performance of …Predictability of monthly temperature and precipitation using automatic time series forecasting methods (2018)Predictability of monthly tem…Improving forecasting accuracy for stock market data using EMD-HW bagging (2018)Improving forecasting accurac…Models for optimising the theta method and their relationship to state space models (2016)Models for optimising the the…Distributions of forecasting errors of forecast combinations: Implications for inventory management (2016)Distributions of forecasting …Simple versus complex selection rules for forecasting many time series (2015)Simple versus complex selecti…Disaggregation & aggregation of time series components: A hybrid forecasting approach using generalized regression neural networks and the theta method (2010)Disaggregation & aggregation …Forecasting with Unobserved Components Time Series Models (2006)Forecasting with Unobserved C…Chapter 7 Forecasting with Unobserved Components Time Series Models (2006)Chapter 7 Forecasting with Un…LOCAL LINEAR FORECASTS USING CUBIC SMOOTHING SPLINES (2005)LOCAL LINEAR FORECASTS USING …25 Years of Iif Time Series Forecasting: A Selective Review (2005)25 Years of Iif Time Series F…Combining forecasts: A review and annotated bibliography (1989)Combining forecasts: A review and annotated bibliography (1990)The tourism forecasting competition (2010)Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction (2011)Forecast combinations: An over 50-year review (2022)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011)A new metric of absolute percentage error for intermittent demand forecasts (2016)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)Automatic Time Series Forecasting: The forecast Package for R (2008)Forecasting at Scale (2017)Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data (2018)One-step ahead forecasting of geophysical processes within a purely statistical framework (2018)FQTSFM: A fuzzy-quantum time series forecasting model (2021)Forecasting principles and practiceAnother look at measures of forecast accuracy (2006)Forecasting of customer demands for production planning by local k -nearest neighbor models (2020)Investigating the accuracy of cross-learning time series forecasting methods (2020)The M5 uncertainty competition: Results, findings and conclusions (2021)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)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Generalized autoregressive conditional heteroskedasticity (1986)Improved supply chain management based on hybrid demand forecasts (2005)Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach (2014)Principles of forecasting : a handbook for researchers and practitioners (2001)The politics of attention: how government prioritizes problems (2006)Long memory relationships and the aggregation of dynamic models (1980)Understanding spurious regressions in econometrics (1986)
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Cited by

  • 2018Predictability of monthly temperature and precipitation using automatic time series forecasting methods136 cited
  • 2019A brief history of forecasting competitions129 cited
  • 2016Distributions of forecasting errors of forecast combinations: Implications for inventory management105 cited
  • 2015Simple versus complex selection rules for forecasting many time series92 cited
  • 2006Chapter 7 Forecasting with Unobserved Components Time Series Models91 cited
  • 2016Models for optimising the theta method and their relationship to state space models73 cited
  • 2006Forecasting with Unobserved Components Time Series Models

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70 cited
  • 2018The inventory performance of forecasting methods: Evidence from the M3 competition data70 cited
  • 2020Reinforcement learning framework for freight demand forecasting to support operational planning decisions65 cited
  • 200525 Years of Iif Time Series Forecasting: A Selective Review61 cited
  • 2018Improving forecasting accuracy for stock market data using EMD-HW bagging49 cited
  • 2020Generalizing the Theta method for automatic forecasting49 cited
  • 2020Real-time demand forecasting for an urban delivery platform49 cited
  • 2021STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones44 cited
  • 2010Disaggregation & aggregation of time series components: A hybrid forecasting approach using generalized regression neural networks and the theta method42 cited
  • 2005LOCAL LINEAR FORECASTS USING CUBIC SMOOTHING SPLINES40 cited