DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction
Y Liu, C Gong, L Yang, Y Chen - Expert Systems with Applications, 2020 - Elsevier
Long-term prediction of multivariate time series is still an important but challenging problem.
The key to solve this problem is capturing (1) the spatial correlations at the same time,(2) the …
The key to solve this problem is capturing (1) the spatial correlations at the same time,(2) the …
FC-GAGA: Fully connected gated graph architecture for spatio-temporal traffic forecasting
BN Oreshkin, A Amini, L Coyle, M Coates - Proceedings of the AAAI …, 2021 - ojs.aaai.org
Forecasting of multivariate time-series is an important problem that has applications in traffic
management, cellular network configuration, and quantitative finance. A special case of the …
management, cellular network configuration, and quantitative finance. A special case of the …
Wind power forecasting methods based on deep learning: A survey
X Deng, H Shao, C Hu, D Jiang… - Computer Modeling in …, 2020 - ingentaconnect.com
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact
on grid operation safety when high permeability intermittent power supply is connected to …
on grid operation safety when high permeability intermittent power supply is connected to …
A memory-network based solution for multivariate time-series forecasting
Multivariate time series forecasting is extensively studied throughout the years with
ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns …
ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns …
An end-to-end adaptive input selection with dynamic weights for forecasting multivariate time series
A multivariate time series forecasting is critical in many applications, such as signal
processing, finance, air quality forecasting, and pattern recognition. In particular …
processing, finance, air quality forecasting, and pattern recognition. In particular …
An advanced system to enhance and optimize delivery operations in a smart logistics environment
Optimization of order dispatch operations and delivery time prediction is a major concern in
supply chains, mainly for e-commerce, which requires the implementation of advanced …
supply chains, mainly for e-commerce, which requires the implementation of advanced …
Bitcoin volatility forecasting with a glimpse into buy and sell orders
Bitcoin is one of the most prominent decentralized digital cryptocurrencies. Ability to
understand which factors drive the fluctuations of the Bitcoin price and to what extent they …
understand which factors drive the fluctuations of the Bitcoin price and to what extent they …
RNN with particle flow for probabilistic spatio-temporal forecasting
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and
financial networks. Many classical statistical models often fall short in handling the …
financial networks. Many classical statistical models often fall short in handling the …
A hybrid method with adaptive sub-series clustering and attention-based stacked residual LSTMs for multivariate time series forecasting
F Liu, Y Lu, M Cai - IEEE Access, 2020 - ieeexplore.ieee.org
Multivariate Time Series Forecasting (MTSF) has recently emerged its growing importance
in many industries. However, how to reduce the influence of the noise components existing …
in many industries. However, how to reduce the influence of the noise components existing …
Deep learning-based demand forecasting for Korean postal delivery service
Proper demand forecasting for postal delivery service can be used for optimal logistic
management, staff scheduling and topology planning. More especially, during special …
management, staff scheduling and topology planning. More especially, during special …