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Multivariate Time Series Forecasting

17 papers with code · Time Series

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Neural Ordinary Differential Equations

NeurIPS 2018 rtqichen/torchdiffeq

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 MULTIVARIATE TIME SERIES IMPUTATION

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

21 Mar 2017laiguokun/LSTNet

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

Temporal Pattern Attention for Multivariate Time Series Forecasting

12 Sep 2018gantheory/TPA-LSTM

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

Latent ODEs for Irregularly-Sampled Time Series

8 Jul 2019YuliaRubanova/latent_ode

Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).

MULTIVARIATE TIME SERIES FORECASTING MULTIVARIATE TIME SERIES IMPUTATION TIME SERIES TIME SERIES CLASSIFICATION

Structured Inference Networks for Nonlinear State Space Models

30 Sep 2016clinicalml/structuredinference

We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks.

MULTIVARIATE TIME SERIES FORECASTING

Patient Subtyping via Time-Aware LSTM Networks

KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017 illidanlab/T-LSTM

We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes.

MULTIVARIATE TIME SERIES FORECASTING

DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting

CIKM ’19, November 3–7, 2019, Beijing, China 2019 bighuang624/DSANet

The difficulty of the task lies in that traditional methods fail to capture complicated nonlinear dependencies between time steps and between multiple time series.

MULTIVARIATE TIME SERIES FORECASTING TIME SERIES

GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series

NeurIPS 2019 edebrouwer/gru_ode_bayes

Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i. e., sampling is irregular both in time and across dimensions)-such as in the case of clinical patient data.

IRREGULAR TIME SERIES MULTIVARIATE TIME SERIES FORECASTING TIME SERIES