Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
A review of irregular time series data handling with gated recurrent neural networks
Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor
systems as well as the continued use of unstructured manual data recording mechanisms …
systems as well as the continued use of unstructured manual data recording mechanisms …
A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data
Nowadays, multivariate time series data are increasingly collected in various real world
systems, eg, power plants, wearable devices, etc. Anomaly detection and diagnosis in …
systems, eg, power plants, wearable devices, etc. Anomaly detection and diagnosis in …
Traffic flow forecasting with spatial-temporal graph diffusion network
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of
spatial-temporal mining applications, such as intelligent traffic control and public risk …
spatial-temporal mining applications, such as intelligent traffic control and public risk …
LSTM-based VAE-GAN for time-series anomaly detection
Z Niu, K Yu, X Wu - Sensors, 2020 - mdpi.com
Time series anomaly detection is widely used to monitor the equipment sates through the
data collected in the form of time series. At present, the deep learning method based on …
data collected in the form of time series. At present, the deep learning method based on …
Joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values
Multivariate time series (MTS) forecasting is widely used in various domains, such as
meteorology and traffic. Due to limitations on data collection, transmission, and storage, real …
meteorology and traffic. Due to limitations on data collection, transmission, and storage, real …
Spatial-temporal convolutional graph attention networks for citywide traffic flow forecasting
Traffic flow prediction plays an important role in many spatial-temporal data applications, eg,
traffic management and urban planning. Various deep learning techniques are developed to …
traffic management and urban planning. Various deep learning techniques are developed to …
Online purchase prediction via multi-scale modeling of behavior dynamics
Online purchase forecasting is of great importance in e-commerce platforms, which is the
basis of how to present personalized interesting product lists to individual customers …
basis of how to present personalized interesting product lists to individual customers …
[HTML][HTML] AFSTGCN: Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network
Abstract The prediction for Multivariate Time Series (MTS) explores the interrelationships
among variables at historical moments, extracts their relevant characteristics, and is widely …
among variables at historical moments, extracts their relevant characteristics, and is widely …
Hierarchically structured transformer networks for fine-grained spatial event forecasting
Spatial event forecasting is challenging and crucial for urban sensing scenarios, which is
beneficial for a wide spectrum of spatial-temporal mining applications, ranging from traffic …
beneficial for a wide spectrum of spatial-temporal mining applications, ranging from traffic …