We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
SOTA for Time Series on Bitcoin-Alpha
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.
Time Series forecasting (univariate and multivariate) is a problem of high complexity due the different patterns that have to be detected in the input, ranging from high to low frequencies ones.
Time series prediction has been studied in a variety of domains.
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.
Timely accurate traffic forecast is crucial for urban traffic control and guidance.
#4 best model for Traffic Prediction on METR-LA
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing.
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
#3 best model for Traffic Prediction on PeMS-M
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.