Time Series Prediction
66 papers with code • 2 benchmarks • 3 datasets
The goal of Time Series Prediction is to infer the future values of a time series from the past.
LibrariesUse these libraries to find Time Series Prediction models and implementations
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.
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing.
In this paper, we present evolutionary state graph, a dynamic graph structure designed to systematically represent the evolving relations (edges) among states (nodes) along time.
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes.