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Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions
16 references·1 citing·41 second hop
Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions (2022)Machine learning applications in hierarch…The M5 competition: Background, organization, and implementation (2021)The M5 competition: Backgroun…Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management (2020)Predicting solutions of large…Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion (2020)Demand forecasting in supply …Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization (2018)Optimal Forecast Reconciliati…Forecasting with temporal hierarchies (2017)Forecasting with temporal hie…A note on the validity of cross-validation for evaluating autoregressive time series prediction (2017)A note on the validity of cro…Supply chain forecasting: Theory, practice, their gap and the future (2015)Supply chain forecasting: The…Fast computation of reconciled forecasts for hierarchical and grouped time series (2015)Fast computation of reconcile…Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information (2015)Demand forecasting with high …Optimal combination forecasts for hierarchical time series (2011)Optimal combination forecasts…Multivariate Statistical Process Control Using LASSO (2009)Multivariate Statistical Proc…forecast: Forecasting Functions for Time Series and Linear Models (2009)forecast: Forecasting Functio…Hierarchical forecasts for Australian domestic tourism (2008)Hierarchical forecasts for Au…Another Look at Forecast Accuracy Metrics for Intermittent Demand (2006)Another Look at Forecast Accu…Disaggregation methods to expedite product line forecasting (1990)Disaggregation methods to exp…Forecasting principles and practiceForecasting principles and pr…Blood donation projections using hierarchical time series forecasting: the case of Zimbabwe’s national blood bank (2024)Blood donation projections us…Forecasting methods and applications (1993)The M3-Competition: results, conclusions and implications (2000)Another look at measures of forecast accuracy (2006)Forecasting monthly and quarterly time series using STL decomposition (2011)Designing Transparency for Effective Human-AI Collaboration (2022)Production and inventory management (1983)Comparing Predictive Accuracy (1994)Comparing Predictive Accuracy (1995)‘Horses for Courses’ in demand forecasting (2014)Tourism forecasting: A review of methodological developments over the last decade (2018)Multivariate Generalizations of Cumulative Sum Quality-Control Schemes (1988)Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements (1990)A Multivariate Exponentially Weighted Moving Average Control Chart (1992)A Multivariate Sign EWMA Control Chart (2011)A Change-Point Approach for Phase-I Analysis in Multivariate Profile Monitoring and Diagnosis (2015)Nonparametric (distribution-free) control charts: An updated overview and some results (2019)Automatic Time Series Forecasting: The forecast Package for R (2008)Retail forecasting: Research and practice (2019)The Combination of Forecasts (1969)Judgmental selection of forecasting models (2018)Forecasting in social settings: The state of the art (2019)Predictive analytics for demand forecasting: A deep learning-based decision support system (2022)Information Distortion in a Supply Chain: The Bullwhip Effect (1997)Supply Chain Management: Strategy, Planning and Operations (2000)Supply Chain Management: Strategy, Planning, and Operation (2003)Literature review: Strategic network optimization models in waste reverse supply chains (2018)Machine learning demand forecasting and supply chain performance (2020)Forecasting with Exponential Smoothing : The State Space Approach (2008)On the use of cross-validation for time series predictor evaluation (2012)Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series (2019)Evaluating time series forecasting models: an empirical study on performance estimation methods (2020)M5 accuracy competition: Results, findings, and conclusions (2022)Prediction, Learning, and Games (2006)Forecast reconciliation: A geometric view with new insights on bias correction (2020)Forecast combinations: An over 50-year review (2022)Operational Research: methods and applications (2023)Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation (2016)The M4 Competition: Results, findings, conclusion and way forward (2018)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)
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References

  • n.d.Forecasting principles and practice4,364 cited
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  • 2006Another Look at Forecast Accuracy Metrics for Intermittent Demand270 cited
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  • 2015Fast computation of reconciled forecasts for hierarchical and grouped time series155 cited
  • 2021The M5 competition: Background, organization, and implementation143 cited

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2020
Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management
141 cited
  • 1990Disaggregation methods to expedite product line forecasting139 cited
  • Cited by

    • 2024Blood donation projections using hierarchical time series forecasting: the case of Zimbabwe’s national blood bank5 cited