We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
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In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series---in particular spatiotemporal data---in the presence of missing values.
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.
Backpropagation through the ODE solver allows each layer to adapt its internal time-step, enabling the network to learn task-relevant time-scales.
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.
#4 best model for Multivariate Time Series Imputation on MuJoCo
Time series prediction has been studied in a variety of domains.
Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes.