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Traffic flow matrix-based graph neural network with attention mechanism for traffic flow prediction
Traffic flow forecasting is of great importance in intelligent transportation systems for
congestion mitigation and intelligent traffic management. Most of the existing methods …
congestion mitigation and intelligent traffic management. Most of the existing methods …
NT-DPTC: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation
Missing traffic data imputation is an important step in the intelligent transportation systems.
Low rank approximation is an important method for the missing traffic data imputation …
Low rank approximation is an important method for the missing traffic data imputation …
[HTML][HTML] Edible oil wholesale price forecasts via the neural network
X Xu, Y Zhang - Energy Nexus, 2023 - Elsevier
For a wide spectrum of agricultural market participants, building price forecasts of various
agricultural commodities has always been a vital project. In this work, we approach this …
agricultural commodities has always been a vital project. In this work, we approach this …
[HTML][HTML] China mainland new energy index price forecasting with the neural network
X Xu, Y Zhang - Energy Nexus, 2023 - Elsevier
For policymakers and investors, forecasting prices of energy indices has always been an
important task. The present work focuses on the Chinese market and explores the daily price …
important task. The present work focuses on the Chinese market and explores the daily price …
Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms
The wholesale electricity market is composed of real-time market and procurement. Since
the fully liberalization of the energy market in Singapore in 2018, competition among the …
the fully liberalization of the energy market in Singapore in 2018, competition among the …
[HTML][HTML] Traffic flow prediction model based on improved variational mode decomposition and error correction
G Li, H Deng, H Yang - Alexandria Engineering Journal, 2023 - Elsevier
With the aggravation of traffic congestion, traffic flow data (TFD) prediction is very important
for traffic managers to control traffic congestion and for traffic participants to plan their trips …
for traffic managers to control traffic congestion and for traffic participants to plan their trips …
[HTML][HTML] Forecasting the traffic flow by using ARIMA and LSTM models: Case of Muhima junction
Traffic operation efficiency is greatly impacted by the increase in travel demand and the
increase in vehicle ownership. The continued increase in traffic demand has rendered the …
increase in vehicle ownership. The continued increase in traffic demand has rendered the …
[HTML][HTML] Modeling high-frequency financial data using R and Stan: A bayesian autoregressive conditional duration approach
Abstract In econometrics, Autoregressive Conditional Duration (ACD) models use high-
frequency economic or financial duration data, which mostly exhibit irregular time intervals …
frequency economic or financial duration data, which mostly exhibit irregular time intervals …
A functional autoregressive approach for modeling and forecasting short-term air temperature
A precise forecast of atmospheric temperatures is essential for various applications such as
agriculture, energy, public health, and transportation. Modern advancements in technology …
agriculture, energy, public health, and transportation. Modern advancements in technology …
Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting
Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance,
which has become the key to avoiding traffic congestion and improving traffic management …
which has become the key to avoiding traffic congestion and improving traffic management …