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On the identification of sales forecasting models in the presence of promotions
16 references·13 citing·36 second hop
On the identification of sales forecasting models in the presence of promotions (2014)On the identification of sales forecastin…Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts (2013)Measuring forecasting accurac…Forecasting the intermittent demand for slow-moving inventories: A modelling approach (2011)Forecasting the intermittent …SKU demand forecasting in the presence of promotions (2009)SKU demand forecasting in the…Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning (2009)Effective forecasting and jud…Forecasting with Exponential Smoothing (2008)Forecasting with Exponential …Forecasting with Exponential Smoothing : The State Space Approach (2008)Forecasting with Exponential …Forecasting and operational research: a review (2008)Forecasting and operational r…Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting (2007)Against Your Better Judgment?…Exponential smoothing: The state of the art—Part II (2006)Exponential smoothing: The st…Another look at measures of forecast accuracy (2006)Another look at measures of f…A field study of sales forecasting accuracy and processes (2000)A field study of sales foreca…Pooled Time Series Analysis (1989)Pooled Time Series AnalysisExponential smoothing: The state of the art (1985)Exponential smoothing: The st…The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)The accuracy of extrapolation…Time Series Analysis: Forecasting and Control. (1978)Time Series Analysis: Forecas…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecas…Multi-Agent Deep Reinforcement Learning for Integrated Demand Forecasting and Inventory Optimization in Sensor-Enabled Retail Supply Chains (2025)Multi-Agent Deep Reinforcemen…The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods (2022)The derived demand for advert…Global models for time series forecasting: A Simulation study (2021)Global models for time series…Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2020)Using shared sell-through dat…Demand forecasting in the presence of systematic events: Cases in capturing sales promotions (2020)Demand forecasting in the pre…Forecasting replenishment orders in retail: value of modelling low and intermittent consumer demand with distributions (2018)Forecasting replenishment ord…A support vector machine for model selection in demand forecasting applications (2018)A support vector machine for …Tactical sales forecasting using a very large set of macroeconomic indicators (2017)Tactical sales forecasting us…Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels? (2017)Demand forecasting by tempora…An application of support vector machines to sales forecasting under promotions (2016)An application of support vec…A novel time-varying bullwhip effect metric: An application to promotional sales (2016)A novel time-varying bullwhip…Good Laboratory Practice for optimization research (2015)Good Laboratory Practice for …Forecasting with multivariate temporal aggregation: The case of promotional modelling (2015)Forecasting with multivariate…Judgment under Uncertainty: Heuristics and Biases (1974)Judgment under Uncertainty: Heuristics and Biases (1975)The illusion of control. (1975)Supply chain forecasting: Theory, practice, their gap and the future (2015)Understanding algorithm aversion: When is advice from automation discounted? 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(1994)Sometimes You Just Have to Leave: Domestic Threats and Forced Migration, 1964-1989 (2003)The decline of the death penalty and the discovery of innocence (2008)Automatic Time Series Forecasting: The forecast Package for R (2008)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)Forecasting with temporal hierarchies (2017)Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2022)Principles of forecasting : a handbook for researchers and practitioners (2001)25 years of time series forecasting (2006)The politics of attention: how government prioritizes problems (2006)A note on the validity of cross-validation for evaluating autoregressive time series prediction (2017)An optimized model using LSTM network for demand forecasting (2020)
this work references cited by second hop

References

  • 1977Time Series Analysis: Forecasting and Control19,200 cited
  • 2006Another look at measures of forecast accuracy5,736 cited
  • 1978Time Series Analysis: Forecasting and Control.5,299 cited
  • 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition1,465 cited
  • 1985Exponential smoothing: The state of the art1,461 cited
  • 2008Forecasting with Exponential Smoothing1,040 cited
  • 2006Exponential smoothing: The state of the art—Part II953 cited
  • 2008Forecasting with Exponential Smoothing : The State Space Approach818 cited
  • 1989Pooled Time Series Analysis465 cited
  • 2009Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning447 cited
  • 2008Forecasting and operational research: a review287 cited
  • 2007Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting225 cited
  • 2013Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts193 cited
  • 2009SKU demand forecasting in the presence of promotions153 cited
  • 2011Forecasting the intermittent demand for slow-moving inventories: A modelling approach

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133 cited
  • 2000A field study of sales forecasting accuracy and processes132 cited
  • Cited by

    • 2015Forecasting with multivariate temporal aggregation: The case of promotional modelling98 cited
    • 2018A support vector machine for model selection in demand forecasting applications91 cited
    • 2021Global models for time series forecasting: A Simulation study83 cited
    • 2015Good Laboratory Practice for optimization research82 cited
    • 2017Tactical sales forecasting using a very large set of macroeconomic indicators71 cited
    • 2020Demand forecasting in the presence of systematic events: Cases in capturing sales promotions70 cited
    • 2022The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods68 cited
    • 2017Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?54 cited
    • 2025Multi-Agent Deep Reinforcement Learning for Integrated Demand Forecasting and Inventory Optimization in Sensor-Enabled Retail Supply Chains51 cited
    • 2020Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains51 cited
    • 2016An application of support vector machines to sales forecasting under promotions41 cited
    • 2018Forecasting replenishment orders in retail: value of modelling low and intermittent consumer demand with distributions40 cited
    • 2016A novel time-varying bullwhip effect metric: An application to promotional sales19 cited