SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
A machine learning approach for forecasting hierarchical time series
16 references·2 citing·38 second hop
A machine learning approach for forecasting hierarchical time series (2021)A machine learning approach for forecasti…A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning (2020)A multi-layer and multi-ensem…Cross-temporal coherent forecasts for Australian tourism (2019)Cross-temporal coherent forec…Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization (2018)Optimal Forecast Reconciliati…Cluster-based hierarchical demand forecasting for perishable goods (2017)Cluster-based hierarchical de…Automatic time series forecasting: the forecast package for R (2017)Automatic time series forecas…Machine Learning Strategies for Time Series Forecasting (2013)Machine Learning Strategies f…Optimal combination forecasts for hierarchical time series (2011)Optimal combination forecasts…Hierarchical forecasts for Australian domestic tourism (2008)Hierarchical forecasts for Au…Automatic Time Series Forecasting: The forecast Package for R (2008)Automatic Time Series Forecas…Chapter 4 Forecast Combinations (2006)Chapter 4 Forecast Combinatio…Another look at measures of forecast accuracy (2006)Another look at measures of f…Another Look at Forecast Accuracy Metrics for Intermittent Demand (2006)Another Look at Forecast Accu…The M3 competition: Statistical tests of the results (2004)The M3 competition: Statistic…Disaggregation methods to expedite product line forecasting (1990)Disaggregation methods to exp…Aggregate Versus Subaggregate Models in Local Area Forecasting (1976)Aggregate Versus Subaggregate…Forecasting principles and practiceForecasting principles and pr…A novel multi-phase hierarchical forecasting approach with machine learning in supply chain management (2023)A novel multi-phase hierarchi…Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives (2021)Cross-temporal forecast recon…Forecasting methods and applications (1993)The M3-Competition: results, conclusions and implications (2000)Forecasting monthly and quarterly time series using STL decomposition (2011)Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion (2020)Designing Transparency for Effective Human-AI Collaboration (2022)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Forecasting with artificial neural networks: (1998)25 years of time series forecasting (2006)Variable Selection in Time Series Forecasting Using Random Forests (2017)Criteria for classifying forecasting methods (2019)Daily retail demand forecasting using machine learning with emphasis on calendric special days (2020)The Combination of Forecasts (1969)Combining forecasts: A review and annotated bibliography (1989)Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2009)Investor Sentiment Aligned: A Powerful Predictor of Stock Returns (2014)Forecasting: theory and practice (2022)Production and inventory management (1983)Comparing Predictive Accuracy (1994)Comparing Predictive Accuracy (1995)‘Horses for Courses’ in demand forecasting (2014)Fast computation of reconciled forecasts for hierarchical and grouped time series (2015)Tourism forecasting: A review of methodological developments over the last decade (2018)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Testing the null hypothesis of stationarity against the alternative of a unit root (1992)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)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)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Supply chain forecasting: Theory, practice, their gap and the future (2015)Retail forecasting: Research and practice (2019)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)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)M5 accuracy competition: Results, findings, and conclusions (2022)
this work references cited by second hop

References

  • 2006Another look at measures of forecast accuracy5,736 cited
  • n.d.Forecasting principles and practice4,364 cited
  • 2008Automatic Time Series Forecasting: The forecast Package for R3,538 cited
  • 2006Chapter 4 Forecast Combinations1,318 cited
  • 2017Automatic time series forecasting: the forecast package for R978 cited
  • 2013Machine Learning Strategies for Time Series Forecasting568 cited
  • 2011Optimal combination forecasts for hierarchical time series471 cited
  • 2018Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization332 cited
  • 2006Another Look at Forecast Accuracy Metrics for Intermittent Demand270 cited
  • 2008Hierarchical forecasts for Australian domestic tourism244 cited
  • 2004The M3 competition: Statistical tests of the results180 cited
  • 1990Disaggregation methods to expedite product line forecasting139 cited
  • 2020A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning136 cited
  • 2017Cluster-based hierarchical demand forecasting for perishable goods101 cited
  • 2019Cross-temporal coherent forecasts for Australian tourism93 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 1976Aggregate Versus Subaggregate Models in Local Area Forecasting81 cited
  • Cited by

    • 2023A novel multi-phase hierarchical forecasting approach with machine learning in supply chain management37 cited
    • 2021Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives36 cited