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 …
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 …
[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 …
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] 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 …
[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 …
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 …
[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 …
Forecasting Fruit Export Damages and Enhancing Food Safety through Risk Management
This study underscores serious issues in the South African fruit export sector, notably
highlighting the persistent fruit damage after 2016 that could boost microbial growth …
highlighting the persistent fruit damage after 2016 that could boost microbial growth …
Spatial autocorrelation of global stock exchanges using functional areal spatial principal component analysis
TH Khoo, D Pathmanathan, S Dabo-Niang - Mathematics, 2023 - mdpi.com
This work focuses on functional data presenting spatial dependence. The spatial
autocorrelation of stock exchange returns for 71 stock exchanges from 69 countries was …
autocorrelation of stock exchange returns for 71 stock exchanges from 69 countries was …