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Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient
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Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient (2022)Using support vector regression and K-nea…Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting (2020)Spatial temporal incidence dy…Multidimensional KNN algorithm based on EEMD and complexity measures in financial time series forecasting (2020)Multidimensional KNN algorith…T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (2019)T-GCN: A Temporal Graph Convo…Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction (2019)Revisiting Spatial-Temporal S…Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity (2018)Short-term traffic forecastin…A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting (2015)A spatiotemporal correlative …Memory properties and fractional integration in transportation time-series (2009)Memory properties and fractio…Predicting stock index increments by neural networks: The role of trading volume under different horizons (2007)Predicting stock index increm…Comparison of parametric and nonparametric models for traffic flow forecasting (2002)Comparison of parametric and …Financial Forecasting Using Support Vector Machines (2001)Financial Forecasting Using S…Book Review: Time Series Analysis: Forecasting and Control (1977)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Testing for a Unit Root in Time Series Regression (1988)Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification (2014)A gradient boosting method to improve travel time prediction (2015)Stock Prices and Volume (1992)Trading Volume and Serial Correlation in Stock Returns (1993)Testing for Linear and Nonlinear Granger Causality in the Stock Price- Volume 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stock price forecasting: From statistical model to deep learning approach (2024)Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks (2021)Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities (2022)
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References

  • 2019T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction3,527 cited
  • 2002Comparison of parametric and nonparametric models for traffic flow forecasting1,016 cited
  • 2019Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction921 cited
  • 2015A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting471 cited
  • 2001Financial Forecasting Using Support Vector Machines392 cited
  • 2020Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting347 cited
  • 2007Predicting stock index increments by neural networks: The role of trading volume under different horizons161 cited
  • 2018Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity154 cited
  • 2009Memory properties and fractional integration in transportation time-series112 cited
  • 2020Multidimensional KNN algorithm based on EEMD and complexity measures in financial time series forecasting89 cited

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