A Survey on Graph Neural Networks for Time Series: Forecasting ...
Jul 7, 2023 · In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.
Awesome Graph Neural Networks for Time Series Analysis (GNN4TS) - GitHub
Jul 11, 2023 · Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting (KDD, 2022) Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning (KDD, 2022) [paper]
Traffic forecasting using graph neural networks and LSTM - Keras
Dec 28, 2021 · This example shows how to forecast traffic condition using graph neural networks and LSTM. Specifically, we are interested in predicting the future values of the traffic speed given a history of the traffic speed for a collection of road segments.
A Step-by-Step Walkthrough Neural Networks for Time-series Forecasting ...
Jun 28, 2022 · how to choose a NN for time-series forecasting; how many past samples are needed to discover a pattern; which is the impact of noise on the prediction quality. It is easy to say “Neural...
Time Series Forecasting with Graph Convolutional Neural Network
Apr 28, 2020 · In this post, I’ve adopted graph neural networks in an uncommon scenario like time series forecasting. In our deep learning model, graph dependency combines itself with the recurrent part trying to provide more accurate forecasts.
Multi-Task Time Series Forecasting Based on Graph Neural Networks …
Jul 28, 2023 · Accurate time series forecasting is of great importance in real-world scenarios such as health care, transportation, and finance. Because of the tendency, temporal variations, and periodicity of the time series data, there are complex and dynamic dependencies among its underlying features.
Multivariate Time-Series Forecasting with Temporal Polynomial Graph …
Modeling multivariate time series (MTS) is critical in modern intelligent systems. The accurate forecast of MTS data is still challenging due to the complicated latent variable correlation. Recent works apply the Graph Neural Networks (GNNs) to the task, with the basic idea of representing the correlation as a static graph.
Financial time series forecasting with multi-modality graph neural network
Jan 1, 2022 · Therefore, in this paper, we propose a multi-modality graph neural network (MAGNN) to learn from these multimodal inputs for financial time series prediction. The heterogeneous graph network is constructed by the sources as nodes and relations in our financial knowledge graph as edges.
Is Graph Neural Networks the next big thing in time series
Oct 28, 2024 · To implement a GNN for time series forecasting, we can use popular graph-based libraries like PyTorch Geometric or DGL (Deep Graph Library), combined with recurrent layers like LSTM to...
Pay Attention to Evolution: Time Series Forecasting With Deep Graph …
The idea is to infer multiple multivariate relationships between co-occurring time-series by assuming that the temporal data depends not only on inner variables and intra-temporal relationships (i.e., observations from itself) but also on outer variables and inter-temporal relationships (i.e., observations from other-selves).
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