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.

Source: Orthogonal Echo State Networks and stochastic evaluations of likelihoods

Libraries

Use these libraries to find Time Series Prediction models and implementations

Most implemented papers

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

microsoft/qlib 7 Apr 2017

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.

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

liyaguang/DCRNN ICLR 2018

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.

Recurrent Neural Networks for Multivariate Time Series with Missing Values

PeterChe1990/GRU-D 6 Jun 2016

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.

Predictive Business Process Monitoring with LSTM Neural Networks

verenich/ProcessSequencePrediction 7 Dec 2016

First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.

GluonTS: Probabilistic Time Series Models in Python

awslabs/gluon-ts 12 Jun 2019

We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.

Deep and Confident Prediction for Time Series at Uber

PawaritL/BayesianLSTM 6 Sep 2017

Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing.

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

VeritasYin/STGCN_IJCAI-18 14 Sep 2017

Timely accurate traffic forecast is crucial for urban traffic control and guidance.

Time-Series Event Prediction with Evolutionary State Graph

VachelHU/ESGRN 10 May 2019

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.

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

LeiBAI/AGCRN NeurIPS 2020

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.

A Critical Review of Recurrent Neural Networks for Sequence Learning

junwang23/deepdirtycodes 29 May 2015

Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes.