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Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand
16 references·11 citing·41 second hop
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(1971)Time Series Analysis: Forecasting and Control (1977)Statistical analysis of cointegration vectors (1988)Testing for a Unit Root in Time Series Regression (1988)Bounds testing approaches to the analysis of level relationships (2001)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)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Forecasting with artificial neural networks: (1998)An artificial neural network (p,d,q) model for timeseries forecasting (2009)An Empirical Comparison of Machine Learning Models for Time Series Forecasting (2010)Forecasting: theory and practice (2022)Textual analysis of stock market prediction using breaking financial news (2009)Twitter mood predicts the stock market (2011)Deep learning-based feature engineering for stock price movement prediction (2018)CNNpred: CNN-based stock market prediction using a diverse set of variables (2019)A Survey of Forex and Stock Price Prediction Using Deep Learning (2021)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Another look at measures of forecast accuracy (2006)An optimized model using LSTM network for demand forecasting (2020)Probabilistic forecasting with temporal convolutional neural network (2020)Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization (2021)Application of support vector machines in financial time series forecasting (2001)Introduction to Time Series and Forecasting (2002)Forecasting Volatility in Financial Markets: A Review (2003)Stock price prediction using deep learning and frequency decomposition (2020)A graph-based CNN-LSTM stock price prediction algorithm with leading indicators (2021)Carbon price forecasting based on CEEMDAN and LSTM (2022)Supply Chain Management: Strategy, Planning, and Operation (2003)An empirically derived agenda of critical research issues for managing supply-chain disruptions (2005)Responsible Leadership and Stakeholder Management: Influence Pathways and Organizational Outcomes (2014)Firm innovation and supply chain resilience: a dynamic capability perspective (2019)Political Parties and Macroeconomic Policy (1977)Seasonal integration and cointegration (1990)Unit root tests in panel data: asymptotic and finite-sample properties (2002)Testing for unit roots in heterogeneous panels (2003)
this work references cited by second hop

References

  • 1979Distribution of the Estimators for Autoregressive Time Series with a Unit Root23,183 cited
  • 1981Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root14,647 cited
  • 1979Distribution of the Estimators for Autoregressive Time Series With a Unit Root9,128 cited
  • 1971Time Series Analysis Forecasting and Control1,792 cited
  • 2003Neural network forecasting for seasonal and trend time series987 cited
  • 2018Time series forecasting of petroleum production using deep LSTM recurrent networks971 cited
  • 2015A LSTM-based method for stock returns prediction: A case study of China stock market734 cited
  • 2018Financial time series forecasting model based on CEEMDAN and LSTM712 cited
  • 2007Application of machine learning techniques for supply chain demand forecasting669 cited
  • 2017Supply Chain Management, A Review386 cited
  • 2005Improved supply chain management based on hybrid demand forecasts312 cited
  • 2006A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets223 cited
  • 2019Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing215 cited
  • 2011A New Introduction to Supply Chains and Supply Chain Management: Definitions and Theories Perspective209 cited

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  • 2020Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail206 cited
  • 2002Forecasting daily foreign exchange rates using genetically optimized neural networks172 cited
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    • 2022Meta-heuristics for sustainable supply chain management: a review101 cited
    • 2023Deploying hybrid modelling to support the development of a digital twin for supply chain master planning under disruptions66 cited
    • 2025Evaluation and prioritization of artificial intelligence integrated block chain factors in healthcare supply chain: A hybrid Decision Making Approach44 cited
    • 2025Leveraging synthetic data to tackle machine learning challenges in supply chains: challenges, methods, applications, and research opportunities40 cited
    • 2025Machine learning in supply chain management: systematic literature review and future research agenda40 cited
    • 2024ChatGPT in supply chains: exploring potential applications, benefits and challenges39 cited
    • 2023Surfing with the tides: how digitalization creates firm performance through supply chain entrainment38 cited
    • 2025Factors influencing the adoption of generative AI in supply chain management: an empirical study based on task-technology fit and the theory of planned behaviour16 cited
    • 2022Application of machine learning techniques for cost estimation of engineer to order products11 cited