Multivariate Time Series Forecasting

27 papers with code • 6 benchmarks • 6 datasets

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Greatest papers with code

Neural Ordinary Differential Equations

rtqichen/torchdiffeq NeurIPS 2018

Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network.

Latent Variable Models Multivariate Time Series Forecasting +1

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

zhouhaoyi/Informer2020 14 Dec 2020

Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning.

Multivariate Time Series Forecasting Time Series +1

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

benedekrozemberczki/pytorch_geometric_temporal 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.

Graph Generation Multivariate Time Series Forecasting +4

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

benedekrozemberczki/pytorch_geometric_temporal 24 May 2020

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.

Graph Learning Multivariate Time Series Forecasting +2

FlowDB a large scale precipitation, river, and flash flood dataset

AIStream-Peelout/flow-forecast 21 Dec 2020

We introduce a novel hourly river flow and precipitation dataset and a second subset of flash flood events with damage estimates and injury counts.

Multivariate Time Series Forecasting

Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting

zalandoresearch/pytorch-ts 28 Jan 2021

In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient.

Latent Variable Models Multivariate Time Series Forecasting +2

Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

zalandoresearch/pytorch-ts ICLR 2021

In this work we model the multivariate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow.

Decision Making Multivariate Time Series Forecasting +3

Temporal Pattern Attention for Multivariate Time Series Forecasting

gantheory/TPA-LSTM 12 Sep 2018

To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism.

Multivariate Time Series Forecasting Time Series +1

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

laiguokun/LSTNet 21 Mar 2017

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.

Multivariate Time Series Forecasting Time Series +1