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time-series-1

Time Series Prediction

3260 papers • 126 benchmarks • 313 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

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Benchmarks

These leaderboards are used to track progress in time-series-prediction-3

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Dataset

Best Model

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Sunspot

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Data Collected with Package Delivery Quadcopter Drone

Data Collected with Package Delivery Quadcopter Drone

Libraries

i

Use these libraries to find time-series-prediction-3 models and implementations

benedekrozemberczki/pytorch_geometr…

2 papers 2,552

Datasets

EarthNet2021

STREETS

ExtMarker

Air Quality Index

Air Quality Index

Data Collected with Package Delivery Quadcopter Drone

Data Collected with Package Delivery Quadcopter Drone

EEG Eye State

Subtasks

No subtasks available.

Most implemented papers

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Yaguang Li, Rose Yu, C. Shahabi, Yan Liu•Wed Jul 05 2017

Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow and evaluates the framework on two real-world large scale road network traffic datasets and observes consistent improvement.

3698

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Box-Jenkins

NBA player performance prediction dataset

NBA player performance prediction dataset

0

Recurrent Neural Networks for Multivariate Time Series with Missing Values

Kyunghyun Cho, D. Sontag, Yan Liu, Zhengping Che, S. Purushotham•Sun Jun 05 2016

Novel deep learning models are developed based on Gated Recurrent Unit, a state-of-the-art recurrent neural network that takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results.

2219 0

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

Wei Cheng, Haifeng Chen, Guofei Jiang, G. Cottrell, Dongjin Song, Yao Qin•Thu Apr 06 2017

A dual-stage attention-based recurrent neural network (DA-RNN) to address the long-term temporal dependencies of the Nonlinear autoregressive exogenous model and can outperform state-of-the-art methods for time series prediction.

1436 0

GluonTS: Probabilistic Time Series Models in Python

A. Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, J. Schulz, Lorenzo Stella, Ali Caner Türkmen, Bernie Wang•Tue Jun 11 2019

Gluon Time Series is introduced, a library for deep-learning-based time series modeling that provides all necessary components and tools that scientists need for quickly building new models, for efficiently running and analyzing experiments and for evaluating model accuracy.

124 0

Deep and Confident Prediction for Time Series at Uber

Lingxue Zhu, N. Laptev•Tue Sep 05 2017

A novel end-to-end Bayesian deep model is proposed that provides time series prediction along with uncertainty estimation at Uber and is successfully applied to large-scale time series anomaly detection at Uber.

366 0

Predictive Business Process Monitoring with LSTM Neural Networks

Niek Tax, I. Verenich, M. Rosa, M. Dumas•Tue Dec 06 2016

This paper investigates Long Short-Term Memory neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks and shows that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.

504 0

Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting

Ting Yu, Haoteng Yin, Zhanxing Zhu•Wed Sep 13 2017

This paper proposes a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain, and builds the model with complete convolutional structures, which enable much faster training speed with fewer parameters.

4553 0

Liquid Time-constant Networks

R. Grosu, Ramin M. Hasani, Mathias Lechner, Alexander Amini, D. Rus•Sun Jun 07 2020

A new class of time-continuous recurrent neural network models that exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks are introduced.

339 0

A Critical Review of Recurrent Neural Networks for Sequence Learning

Zachary Chase Lipton•Thu May 28 2015

The goal of this survey is to provide a selfcontained explication of the state of the art of recurrent neural networks together with a historical perspective and references to primary research.

2561 0

Time-Series Event Prediction with Evolutionary State Graph

Carl Yang, Wenjie Hu, Yang Yang, Ziqiang Cheng, Xiang Ren•Tue Nov 24 2020

This paper presents evolutionary state graph, a dynamic graph structure designed to systematically represent the evolving relations (edges) among states (nodes) along time, and proposes a novel graph neural network model, Evolutionary State Graph Network (EvoNet), to encode the evolutionary stategraph for accurate and interpretable time-series event prediction.

34 0

Adding a benchmark result helps the community track progress.

Time Series Prediction | State-of-the-Art