About
The goal of Time Series Prediction is to infer the future values of a time series from the past.
Source: Orthogonal Echo State Networks and stochastic evaluations of likelihoods
Benchmarks
Greatest papers with code
GluonTS: Probabilistic Time Series Models in Python
• awslabs/gluon-ts
• 
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
ANOMALY DETECTION TIME SERIES TIME SERIES FORECASTING TIME SERIES PREDICTION
Liquid Time-constant Networks
We introduce a new class of time-continuous recurrent neural network models.
Bayesian Temporal Factorization for Multidimensional Time Series Prediction
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.
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
Ranked #3 on
Traffic Prediction
on PEMS-BAY
MULTIVARIATE TIME SERIES FORECASTING SPATIO-TEMPORAL FORECASTING TIME SERIES TIME SERIES PREDICTION TRAFFIC PREDICTION
Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting
In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework to model multivariate time series data.
IMPUTATION MULTIVARIATE TIME SERIES FORECASTING TIME SERIES TIME SERIES PREDICTION
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Timely accurate traffic forecast is crucial for urban traffic control and guidance.
Ranked #4 on
Traffic Prediction
on PeMS-M
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
• Zhenye-Na/DA-RNN
• 
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.
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
NeurIPS 2019
• abr/neurips2019
• 
Backpropagation through the ODE solver allows each layer to adapt its internal time-step, enabling the network to learn task-relevant time-scales.
Ranked #2 on
Sequential Image Classification
on Sequential MNIST
SEQUENTIAL IMAGE CLASSIFICATION TIME SERIES TIME SERIES PREDICTION
Recurrent Neural Networks for Multivariate Time Series with Missing Values
• Han-JD/GRU-D
•
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
Ranked #4 on
Multivariate Time Series Forecasting
on MuJoCo
MULTIVARIATE TIME SERIES FORECASTING MULTIVARIATE TIME SERIES IMPUTATION TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLASSIFICATION TIME SERIES PREDICTION
Predictive Business Process Monitoring with LSTM Neural Networks
First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.
MULTIVARIATE TIME SERIES FORECASTING PREDICTIVE PROCESS MONITORING TIME SERIES PREDICTION