SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Exploring the use of deep neural networks for sales forecasting in fashion retail
14 references·16 citing·31 second hop
Exploring the use of deep neural networks for sales forecasting in fashion retail (2018)Exploring the use of deep neural networks…Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies (2017)Deep learning networks for st…Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning (2009)Effective forecasting and jud…Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models (2005)Sensitivity Analysis in Pract…Stock-Outs Cause Walkouts (2004)Stock-Outs Cause WalkoutsSensitivity Analysis in Practice: A Guide to Assessing Scientific Models (2004)Sensitivity Analysis in Pract…Information Distortion in a Supply Chain: The Bullwhip Effect (1997)Information Distortion in a S…Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model (1996)Market share forecasting: An …Fuzzy sets (1996)Fuzzy setsStatistical Methods for Forecasting. (1984)Statistical Methods for Forec…Statistical Methods for Forecasting (1983)Statistical Methods for Forec…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecas…Fuzzy sets (1965)Fuzzy setsSmoothing, Forecasting and Prediction of Discrete Time Series. (1964)Smoothing, Forecasting and Pr…Forecasting Sales by Exponentially Weighted Moving Averages (1960)Forecasting Sales by Exponent…Demand forecasting for fashion products: A systematic review (2023)Demand forecasting for fashio…Time-series forecasting of seasonal items sales using machine learning – A comparative analysis (2022)Time-series forecasting of se…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…Predictive analytics for demand forecasting: A deep learning-based decision support system (2022)Predictive analytics for dema…LSTM with particle Swam optimization for sales forecasting (2022)LSTM with particle Swam optim…Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales (2022)Deep-learning model using hyb…A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach (2022)A Comparative Study of Demand…Understanding consumer engagement in social media: The role of product lifecycle (2021)Understanding consumer engage…Product backorder prediction using deep neural network on imbalanced data (2021)Product backorder prediction …Forecasting the macrolevel determinants of entrepreneurial opportunities using artificial intelligence models (2021)Forecasting the macrolevel de…Prediction-Based Portfolio Optimization Models Using Deep Neural Networks (2020)Prediction-Based Portfolio Op…From predictive to prescriptive analytics: A data-driven multi-item newsvendor model (2020)From predictive to prescripti…Forecasting demand profiles of new products (2020)Forecasting demand profiles o…A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network (2020)A hybrid stock price index fo…A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches (2020)A data-driven forecasting app…Portfolio formation with preselection using deep learning from long-term financial data (2019)Portfolio formation with pres…Does the Stock Market Overreact? (1985)Hierarchical structure in financial markets (1999)Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? (2007)A systematic review of fundamental and technical analysis of stock market predictions (2019)CNNpred: CNN-based stock market prediction using a diverse set of variables (2019)Applications of deep learning in stock market prediction: Recent progress (2021)Low frequency filtering and real business cycles (1993)An overview of methods for determining OWA weights (2005)Exponential smoothing: The state of the art—Part II (2006)Handbook on Constructing Composite Indicators (2005)An effective screening design for sensitivity analysis of large models (2007)Handbook on Constructing Composite Indicators: Methodology and User Guide (2008)Arcs of integration: an international study of supply chain strategies (2001)DEFINING SUPPLY CHAIN MANAGEMENT (2001)Perspectives in supply chain risk management (2006)Exponential smoothing: The state of the art (1985)25 years of time series forecasting (2006)Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011)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)Calculating Interval Forecasts (1993)The predictive validation of ecological and environmental models (1993)The relation of flow-experience and physiological arousal under stress — Can u shape it? (2014)Extensions of the TOPSIS for group decision-making under fuzzy environment (2000)An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field (2010)Science mapping software tools: Review, analysis, and cooperative study among tools (2011)Tools for Composite Indicators Building (2005)Family resemblances: Studies in the internal structure of categories (1975)Basic objects in natural categories (1976)Soft set theory—First results (1999)
this work references cited by second hop

References

  • 1965Fuzzy sets67,827 cited
  • 1996Fuzzy sets46,880 cited
  • 1977Time Series Analysis: Forecasting and Control19,200 cited
  • 1997Information Distortion in a Supply Chain: The Bullwhip Effect4,718 cited
  • 1960Forecasting Sales by Exponentially Weighted Moving Averages2,117 cited
  • 2005Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models1,843 cited
  • 2004Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models1,797 cited
  • 1964Smoothing, Forecasting and Prediction of Discrete Time Series.968 cited
  • 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies833 cited
  • 1983Statistical Methods for Forecasting622 cited
  • 1984Statistical Methods for Forecasting.542 cited
  • 2009Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning447 cited
  • 2004Stock-Outs Cause Walkouts159 cited
  • 1996Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model151 cited

Cited by

  • 2019Portfolio formation with preselection using deep learning from long-term financial data

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

219 cited
  • 2022Time-series forecasting of seasonal items sales using machine learning – A comparative analysis217 cited
  • 2020A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network191 cited
  • 2021Understanding consumer engagement in social media: The role of product lifecycle143 cited
  • 2022Predictive analytics for demand forecasting: A deep learning-based decision support system114 cited
  • 2020Prediction-Based Portfolio Optimization Models Using Deep Neural Networks104 cited
  • 2022A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach103 cited
  • 2020Forecasting demand profiles of new products94 cited
  • 2022LSTM with particle Swam optimization for sales forecasting93 cited
  • 2020A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches91 cited
  • 2021Product backorder prediction using deep neural network on imbalanced data89 cited
  • 2022Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales69 cited
  • 2021Forecasting the macrolevel determinants of entrepreneurial opportunities using artificial intelligence models68 cited
  • 2022The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods68 cited
  • 2020From predictive to prescriptive analytics: A data-driven multi-item newsvendor model63 cited
  • 2023Demand forecasting for fashion products: A systematic review60 cited