DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series

• URL: http://arxiv.org/abs/2405.18693v1
• Date: Wed, 29 May 2024 02:06:17 GMT
• Title: DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series
• Authors: Abishek Sriramulu, Nicolas Fourrier, Christoph Bergmeir,
• Abstract summary: This paper introduces a novel Hierarchical GNN (DeepHGNN) framework, explicitly designed for forecasting in complex hierarchical structures. DeepHGNN ensures forecast accuracy and coherence across various hierarchical levels while sharing signals across them. Our comprehensive evaluation set against several state-of-the-art models confirm the superior performance of DeepHGNN.
• Score: 5.029860184826624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlations and inter-series relationships. This paper introduces a novel Hierarchical GNN (DeepHGNN) framework, explicitly designed for forecasting in complex hierarchical structures. The uniqueness of DeepHGNN lies in its innovative graph-based hierarchical interpolation and an end-to-end reconciliation mechanism. This approach ensures forecast accuracy and coherence across various hierarchical levels while sharing signals across them, addressing a key challenge in hierarchical forecasting. A critical insight in hierarchical time series is the variance in forecastability across levels, with upper levels typically presenting more predictable components. DeepHGNN capitalizes on this insight by pooling and leveraging knowledge from all hierarchy levels, thereby enhancing the overall forecast accuracy. Our comprehensive evaluation set against several state-of-the-art models confirm the superior performance of DeepHGNN. This research not only demonstrates DeepHGNN's effectiveness in achieving significantly improved forecast accuracy but also contributes to the understanding of graph-based methods in hierarchical time series forecasting.

Related papers

• Informed deep hierarchical classification: a non-standard analysis inspired approach [0.0]
  It consists in a multi-output deep neural network equipped with specific projection operators placed before each output layer. The design of such an architecture, called lexicographic hybrid deep neural network (LH-DNN), has been possible by combining tools from different and quite distant research fields. To assess the efficacy of the approach, the resulting network is compared against the B-CNN, a convolutional neural network tailored for hierarchical classification tasks.
  arXiv  Detail & Related papers  (2024-09-25T14:12:50Z)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Probabilistic Forecasting with Coherent Aggregation [42.215158938066054]
  We augment an MQForecaster neural network architecture with a novel deep Gaussian factor forecasting model that achieves coherence by construction. In a comparison to state-of-the-art coherent forecasting methods, DeepCoFactor achieves significant improvements in scaled CRPS forecast accuracy, with average gains of 15%.
  arXiv  Detail & Related papers  (2023-07-19T07:31:37Z)
• Uncertainty Quantification over Graph with Conformalized Graph Neural Networks [52.20904874696597]
  Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. GNNs lack rigorous uncertainty estimates, limiting their reliable deployment in settings where the cost of errors is significant. We propose conformalized GNN (CF-GNN), extending conformal prediction (CP) to graph-based models for guaranteed uncertainty estimates.
  arXiv  Detail & Related papers  (2023-05-23T21:38:23Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Semantic Graph Neural Network with Multi-measure Learning for Semi-supervised Classification [5.000404730573809]
  Graph Neural Networks (GNNs) have attracted increasing attention in recent years. Recent studies have shown that GNNs are vulnerable to the complex underlying structure of the graph. We propose a novel framework for semi-supervised classification.
  arXiv  Detail & Related papers  (2022-12-04T06:17:11Z)
• Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification [24.91896527342631]
  Graph convolutional network (GCN) based models have shown impressive performance. Deep learning frameworks based on point estimation suffer from low generalization and inability to quantify the classification results uncertainty. In this paper, we propose a Bayesian layer with Bayesian idea as an insertion layer into point estimation based neural networks. A Generative Adversarial Network (GAN) is built to solve the sample imbalance problem of HSI dataset.
  arXiv  Detail & Related papers  (2022-11-14T12:56:56Z)
• Simple and Efficient Heterogeneous Graph Neural Network [55.56564522532328]
  Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) over homogeneous graphs, especially the attention mechanism and the multi-layer structure. This paper conducts an in-depth and detailed study of these mechanisms and proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN)
  arXiv  Detail & Related papers  (2022-07-06T10:01:46Z)
• EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks [51.42338058718487]
  Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs. We propose EvenNet, a spectral GNN corresponding to an even-polynomial graph filter.
  arXiv  Detail & Related papers  (2022-05-27T10:48:14Z)
• Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis [94.64007376939735]
  We theoretically characterize the impact of connectivity patterns on the convergence of deep neural networks (DNNs) under gradient descent training. We show that by a simple filtration on "unpromising" connectivity patterns, we can trim down the number of models to evaluate.
  arXiv  Detail & Related papers  (2022-05-11T17:43:54Z)
• Understanding the Distributions of Aggregation Layers in Deep Neural Networks [8.784438985280092]
  aggregation functions as an important mechanism for consolidating deep features into a more compact representation. In particular, the proximity of global aggregation layers to the output layers of DNNs mean that aggregated features have a direct influence on the performance of a deep net. We propose a novel mathematical formulation for analytically modelling the probability distributions of output values of layers involved with deep feature aggregation.
  arXiv  Detail & Related papers  (2021-07-09T14:23:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.