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Well googled is half done: Multimodal forecasting of new fashion product sales with image‐based google trends
16 references·0 citing·40 second hop
Well googled is half done: Multimodal forecasting of new fashion product sales with image‐based google trends (2024)Well googled is half done: Multimodal for…Production and Operations Analysis (2019)Production and Operations Ana…Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends (2019)Googling Fashion: Forecasting…Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. (2016)Comparing the Pearson and Spe…Forecasting volatility with empirical similarity and Google Trends (2015)Forecasting volatility with e…Chapter 3 - The Future of Prediction (2015)Chapter 3 - The Future of Pre…Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach (2014)Can Google data improve the f…Searching for the picture: forecasting UK cinema admissions using Google Trends data (2011)Searching for the picture: fo…Internet search behavior as an economic forecasting tool: The case of inflation expectations (2011)Internet search behavior as a…Forecasting with Exponential Smoothing (2008)Forecasting with Exponential …Forecasting with Exponential Smoothing : The State Space Approach (2008)Forecasting with Exponential …Efficient Supply Contracts for Fashion Goods with Forecast Updating and Two Production Modes (2000)Efficient Supply Contracts fo…Testing the null hypothesis of stationarity against the alternative of a unit root (1992)Testing the null hypothesis o…Production and Operations Analysis (1992)Production and Operations Ana…Smoothing, Forecasting, and Prediction of Discrete Time Series (1965)Smoothing, Forecasting, and P…Smoothing, Forecasting and Prediction of Discrete Time Series. (1964)Smoothing, Forecasting and Pr…Forecasting principles and practiceForecasting principles and pr…Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)Testing for a Unit Root in Time Series Regression (1988)A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test (1999)Unit root tests for panel data (2001)Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework (2014)Managing Channel Profits (1983)Quantitative Models for Supply Chain Management (1999)Contracting to Assure Supply: How to Share Demand Forecasts in a Supply Chain (2001)Closed-Loop Supply Chain Models with Product Remanufacturing (2004)Perspectives in supply chain risk management (2006)Reverse Channel Design: The Case of Competing Retailers (2006)Comparing Predictive Accuracy (1994)Comparing Predictive Accuracy (1995)In Search of Attention (2011)New developments in tourism and hotel demand modeling and forecasting (2017)Forecasting sales in the supply chain: Consumer analytics in the big data era (2018)Forecasting tourism demand with multisource big data (2020)The Proof and Measurement of Association between Two Things (1904)On the Theory of Scales of Measurement (1946)Rank Correlation Methods. (1957)Sensing sociability: Individual differences in young adults’ conversation, calling, texting, and app use behaviors in daily life. (2019)Transparency and replicability in qualitative research: The case of interviews with elite informants (2019)Myths About Linear and Monotonic Associations: Pearson’s r , Spearman’s ρ , and Kendall’s τ (2021)Commissioned Paper: Capacity Management, Investment, and Hedging: Review and Recent Developments (2003)Group Buying on the Web: A Comparison of Price-Discovery Mechanisms (2003)Quality Improvement Incentives and Product Recall Cost Sharing Contracts (2009)A pragmatic view of accuracy measurement in forecasting (1986)A dynamic artificial neural network model for forecasting time series events (2004)Sales forecasts in clothing industry: The key success factor of the supply chain management (2010)Forecasting and operational research: a review (2008)Forecasting with temporal hierarchies (2017)Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2022)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)Sources of Supply Chain Disruptions, Factors That Breed Vulnerability, and Mitigating Strategies (2009)A note on the validity of cross-validation for evaluating autoregressive time series prediction (2017)A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2019)An optimized model using LSTM network for demand forecasting (2020)
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References

  • 1992Testing the null hypothesis of stationarity against the alternative of a unit root12,634 cited
  • n.d.Forecasting principles and practice4,364 cited
  • 1992Production and Operations Analysis1,213 cited
  • 2016Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data.1,198 cited
  • 2008Forecasting with Exponential Smoothing1,040 cited
  • 2019Production and Operations Analysis938 cited
  • 2008Forecasting with Exponential Smoothing : The State Space Approach818 cited
  • 2000Efficient Supply Contracts for Fashion Goods with Forecast Updating and Two Production Modes517 cited
  • 2014Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach331 cited
  • 1965Smoothing, Forecasting, and Prediction of Discrete Time Series264 cited
  • 2011Internet search behavior as an economic forecasting tool: The case of inflation expectations146 cited
  • 2015Chapter 3 - The Future of Prediction138 cited
  • 2015Forecasting volatility with empirical similarity and Google Trends120 cited
  • 2019Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends114 cited
  • 2011

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Searching for the picture: forecasting UK cinema admissions using Google Trends data
97 cited
  • 1964Smoothing, Forecasting and Prediction of Discrete Time Series.93 cited
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