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Forecasting with trees
14 references·4 citing·33 second hop
Forecasting with trees (2021)Forecasting with treesAion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics (2023)Aion Framework: Dimensional E…Forecasting: theory and practice (2022)Forecasting: theory and pract…Principles and algorithms for forecasting groups of time series: Locality and globality (2021)Principles and algorithms for…Kaggle forecasting competitions: An overlooked learning opportunity (2020)Kaggle forecasting competitio…Sales Demand Forecast in E-commerce Using a Long Short-Term Memory Neural Network Methodology (2019)Sales Demand Forecast in E-co…N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019)N-BEATS: Neural basis expansi…FFORMA: Feature-based forecast model averaging (2019)FFORMA: Feature-based forecas…DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)DeepAR: Probabilistic forecas…Criteria for classifying forecasting methods (2019)Criteria for classifying fore…A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2019)A hybrid method of exponentia…Probabilistic demand forecasting at scale (2017)Probabilistic demand forecast…A Multi-Horizon Quantile Recurrent Forecaster (2017)A Multi-Horizon Quantile Recu…Intermittent demand forecasts with neural networks (2013)Intermittent demand forecasts…Forecasting principles and practiceForecasting principles and pr…Simplifying tree-based methods for retail sales forecasting with explanatory variables (2023)Simplifying tree-based method…Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models (2023)Does institutional quality af…Applying Machine Learning in Retail Demand Prediction—A Comparison of Tree-Based Ensembles and Long Short-Term Memory-Based Deep Learning (2023)Applying Machine Learning in …Time Series and Regression Methods for Univariate Environmental Forecasting: An Empirical Evaluation (2022)Time Series and Regression Me…Forecasting with artificial neural networks: (1998)Out-of-sample tests of forecasting accuracy: an analysis and review (2000)Another look at measures of forecast accuracy (2006)Forecast combinations for intermittent demand (2014)Single-hidden layer neural networks for forecasting intermittent demand (2016)UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution (2019)Designing and managing the supply chain : concepts, strategies, and case studies (2000)Automatic Time Series Forecasting: The forecast Package for R (2008)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Time Series Analysis: Forecasting and Control. (1978)A quantile regression neural network approach to estimating the conditional density of multiperiod returns (2000)Inventory – forecasting: Mind the gap (2021)The Combination of Forecasts (1969)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Combining forecasts: A review and annotated bibliography (1989)Forecast combinations: An over 50-year review (2022)A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017)The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms (2017)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Fuzzy sets (1965)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)User Acceptance of Computer Technology: A Comparison of Two Theoretical Models (1989)Forecast evaluation for data scientists: common pitfalls and best practices (2022)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2022)M5 accuracy competition: Results, findings, and conclusions (2022)Comparison of statistical and machine learning methods for daily SKU demand forecasting (2020)The M5 competition: Background, organization, and implementation (2021)Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward (2022)ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing (2023)ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope (2023)Chatting and cheating: Ensuring academic integrity in the era of ChatGPT (2023)
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References

  • 2023Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics13,695 cited
  • n.d.Forecasting principles and practice4,364 cited
  • 2019DeepAR: Probabilistic forecasting with autoregressive recurrent networks2,726 cited
  • 2022Forecasting: theory and practice942 cited
  • 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecasting769 cited
  • 2019A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting766 cited
  • 2019FFORMA: Feature-based forecast model averaging313 cited
  • 2017A Multi-Horizon Quantile Recurrent Forecaster272 cited
  • 2020Kaggle forecasting competitions: An overlooked learning opportunity266 cited
  • 2013Intermittent demand forecasts with neural networks231 cited
  • 2019Criteria for classifying forecasting methods210 cited
  • 2021Principles and algorithms for forecasting groups of time series: Locality and globality202 cited
  • 2019Sales Demand Forecast in E-commerce Using a Long Short-Term Memory Neural Network Methodology175 cited
  • 2017Probabilistic demand forecasting at scale126 cited

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  • 2023

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Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models
75 cited
  • 2022Time Series and Regression Methods for Univariate Environmental Forecasting: An Empirical Evaluation65 cited
  • 2023Applying Machine Learning in Retail Demand Prediction—A Comparison of Tree-Based Ensembles and Long Short-Term Memory-Based Deep Learning57 cited
  • 2023Simplifying tree-based methods for retail sales forecasting with explanatory variables40 cited