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

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Datasets

Latest papers with code

Dynamic Virtual Graph Significance Networks for Predicting Influenza

16 Feb 2021aI-area/DVGSN

In this study, we develop a novel method, Dynamic Virtual Graph Significance Networks (DVGSN), which can supervisedly and dynamically learn from similar "infection situations" in historical timepoints.

REPRESENTATION LEARNING TIME SERIES TIME SERIES PREDICTION

0
16 Feb 2021

Recursive Tree Grammar Autoencoders

3 Dec 2020bpaassen/rtgae

Machine learning on tree data has been mostly focused on trees as input.

DRUG DISCOVERY TIME SERIES TIME SERIES PREDICTION

0
03 Dec 2020

ATCN: Agile Temporal Convolutional Networks for Processing of Time Series on Edge

10 Nov 2020TeCSAR-UNCC/ATCN

It makes fundamental improvements over the mainstream temporal convolutional neural networks, including the incorporation of separable depth-wise convolution to reduce the computational complexity of the model and residual connections as time attention machines, increase the network depth and accuracy.

HEARTBEAT CLASSIFICATION TIME SERIES TIME SERIES PREDICTION

3
10 Nov 2020

Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting

10 Sep 2020ostadabbas/DSARF

We introduce deep switching auto-regressive factorization (DSARF), a deep generative model for spatio-temporal data with the capability to unravel recurring patterns in the data and perform robust short- and long-term predictions.

TIME SERIES TIME SERIES FORECASTING TIME SERIES PREDICTION VARIATIONAL INFERENCE WEATHER FORECASTING

6
10 Sep 2020

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

NeurIPS 2020 LeiBAI/AGCRN

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.

GRAPH GENERATION MULTIVARIATE TIME SERIES FORECASTING SPATIO-TEMPORAL FORECASTING TIME SERIES TIME SERIES PREDICTION TRAFFIC PREDICTION

63
06 Jul 2020

Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning

23 Jun 2020anhtuan5696/TPAMTL

Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss.

KNOWLEDGE GRAPHS MULTI-TASK LEARNING TIME SERIES TIME SERIES PREDICTION

5
23 Jun 2020

Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting

18 Jun 2020xinychen/transdim

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

501
18 Jun 2020

Reservoir Computing meets Recurrent Kernels and Structured Transforms

NeurIPS 2020 rubenohana/Reservoir-computing-kernels

Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where internal weights are fixed at random and only a linear output layer is trained.

TIME SERIES TIME SERIES PREDICTION

2
12 Jun 2020

Liquid Time-constant Networks

8 Jun 2020mlech26l/keras-ncp

We introduce a new class of time-continuous recurrent neural network models.

TIME SERIES TIME SERIES PREDICTION

802
08 Jun 2020

DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting

26 May 2020alexmonti19/dagnet

Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment.

HUMAN MOTION PREDICTION MULTI-FUTURE TRAJECTORY PREDICTION TIME SERIES ANALYSIS TIME SERIES PREDICTION TRAJECTORY FORECASTING

43
26 May 2020