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TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction
5 references·0 citing·22 second hop
TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction (2018)TADA: Trend Alignment with Dual-Attention…A Survey of Location Prediction on Twitter (2018)A Survey of Location Predicti…A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017)A Dual-Stage Attention-Based …A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017)A deep learning framework for…Application of machine learning techniques for supply chain demand forecasting (2007)Application of machine learni…Time Series Analysis Forecasting and Control (1971)Time Series Analysis Forecast…Information Distortion in a Supply Chain: The Bullwhip Effect (1997)The Bullwhip Effect In Supply Chains 1 (1997)Information systems in supply chain integration and management (2003)Artificial intelligence in supply chain management: theory and applications (2009)Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities (2020)Artificial intelligence applications in supply chain management (2021)Spatial statistics (1981)Birds of a Feather: Homophily in Social Networks (2001)Friendship and mobility (2011)Twitter as a tool for the management and analysis of emergency situations: A systematic literature review (2018)Data-Driven Computational Social Science: A Survey (2020)Future directions in human mobility science (2023)Forecasting stock market movement direction with support vector machine (2004)CNNpred: CNN-based stock market prediction using a diverse set of variables (2019)Temporal Relational Ranking for Stock Prediction (2019)Applications of deep learning in stock market prediction: Recent progress (2021)Political Parties and Macroeconomic Policy (1977)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Seasonal integration and cointegration (1990)A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2019)N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)
this work references cited by second hop

References

  • 1971Time Series Analysis Forecasting and Control1,792 cited
  • 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction1,646 cited
  • 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory1,131 cited
  • 2007Application of machine learning techniques for supply chain demand forecasting669 cited
  • 2018A Survey of Location Prediction on Twitter248 cited

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