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Deep Learning Based Forecasting: A Case Study from the Online Fashion Industry
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Deep Learning Based Forecasting: A Case Study from the Online Fashion Industry (2023)Deep Learning Based Forecasting: A Case S…Promotheus (2022)PromotheusDeep Learning for Time Series Forecasting: Tutorial and Literature Survey (2022)Deep Learning for Time Series…Robust recurrent network model for intermittent time-series forecasting (2021)Robust recurrent network mode…Principles and algorithms for forecasting groups of time series: Locality and globality (2021)Principles and algorithms for…Pricing and Revenue Optimization (2021)Pricing and Revenue Optimizat…Large-Scale Price Optimization for an Online Fashion Retailer (2021)Large-Scale Price Optimizatio…Forecasting with trees (2021)Forecasting with treesForecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes (2021)Forecasting intermittent and …Retail forecasting: Research and practice (2019)Retail forecasting: Research …Probabilistic Forecasting with Spline Quantile Function RNNs (2019)Probabilistic Forecasting wit…N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019)N-BEATS: Neural basis expansi…DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)DeepAR: Probabilistic forecas…Criteria for classifying forecasting methods (2019)Criteria for classifying fore…Classical and Contemporary Approaches to Big Time Series Forecasting (2019)Classical and Contemporary Ap…Probabilistic demand forecasting at scale (2017)Probabilistic demand forecast…A Multi-Horizon Quantile Recurrent Forecaster (2017)A Multi-Horizon Quantile Recu…Time Series Analysis Forecasting and Control. (1971)Time Series Analysis: Forecasting and Control. (1978)Forecasting principles and practice (2018)Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting (2020)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Forecasting: Methods and Applications. (1979)Forecasting methods and applications (1993)Neural forecasting: Introduction and literature overview. (2020)MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model (2025)Conditional Time Series Forecasting with Convolutional Neural Networks (2017)Forecasting: theory and practice (2022)Forecasting Using Principal Components From a Large Number of Predictors (2002)Another look at measures of forecast accuracy (2006)The Effect of Word of Mouth on Sales: Online Book Reviews (2006)Application of Artificial Intelligence in Automation of Supply Chain Management (2019)M5 accuracy competition: Results, findings, and conclusions (2022)Book Review: Time Series Analysis: Forecasting and Control (1977)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Probabilistic forecasting with temporal convolutional neural network (2020)Automatic Time Series Forecasting: The forecast Package for R (2008)Comparing Predictive Accuracy (1994)The M5 competition: Background, organization, and implementation (2021)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward (2022)Time Series and Regression Methods for Univariate Environmental Forecasting: An Empirical Evaluation (2022)Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics (2023)Applying Machine Learning in Retail Demand Prediction—A Comparison of Tree-Based Ensembles and Long Short-Term Memory-Based Deep Learning (2023)Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models (2023)Time series forecasting using a hybrid ARIMA and neural network model (2003)Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs) (2023)Chaos theory meets deep learning: A new approach to time series forecasting (2024)Evaluating the Effectiveness of Time Series Transformers for Demand Forecasting in Retail (2024)
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References

  • 2019DeepAR: Probabilistic forecasting with autoregressive recurrent networks2,851 cited
  • 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecasting1,149 cited
  • 2019Retail forecasting: Research and practice470 cited
  • 2022Deep Learning for Time Series Forecasting: Tutorial and Literature Survey380 cited
  • 2017A Multi-Horizon Quantile Recurrent Forecaster272 cited
  • 2019Criteria for classifying forecasting methods210 cited
  • 2021Principles and algorithms for forecasting groups of time series: Locality and globality202 cited
  • 2017Probabilistic demand forecasting at scale126 cited
  • 2021Forecasting with trees117 cited
  • 2019Probabilistic Forecasting with Spline Quantile Function RNNs94 cited
  • 2021Pricing and Revenue Optimization62 cited
  • 2021Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes37 cited
  • 2019Classical and Contemporary Approaches to Big Time Series Forecasting37 cited
  • 2021Robust recurrent network model for intermittent time-series forecasting35 cited
  • 2022Promotheus11 cited

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2021
Large-Scale Price Optimization for an Online Fashion Retailer
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