We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
In this paper, we propose to represent time-varying relations among intrinsic factors of time series data by means of an evolutionary state graph structure.
However, as the mobile data of vehicles has been widely collected by sensor-embedded devices in transportation systems, it is possible to predict the traffic flow by analysing mobile data.
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
SOTA for Traffic Prediction on METR-LA
The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades.
First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes.