Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Relationships among time series can be exploited as inductive biases in learning effective forecasting models. In hierarchical time series, relationships among subsets of sequences induce hard constraints (hierarchical inductive biases) on the predicted values. In this paper, we propose a graph-based methodology to unify relational and hierarchical inductive biases in the context of deep learning for time series forecasting. In particular, we model both types of relationships as dependencies in a pyramidal graph structure, with each pyramidal layer corresponding to a level of the hierarchy. By exploiting modern - trainable - graph pooling operators we show that the hierarchical structure, if not available as a prior, can be learned directly from data, thus obtaining cluster assignments aligned with the forecasting objective. A differentiable reconciliation stage is incorporated into the processing architecture, allowing hierarchical constraints to act both as an architectural bias as well as a regularization element for predictions. Simulation results on representative datasets show that the proposed method compares favorably against the state of the art.
Code (1)
Tasks
ClusteringTime SeriesTime Series ClusteringTime Series ForecastingSimilar Papers 제목 키워드 기반
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
We propose a novel approach to the problem of clustering hierarchically aggregated time-series data, which has remained an understudied problem though it has several commercial applications. We first group time series at…
ClusteringTime SeriesTime Series AnalysisDeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series
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 …
Graph Neural NetworkTime SeriesTime Series ForecastingMHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation lea…
ClusteringContrastive LearningRepresentation LearningTime Series+1DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting
Multivariate time series forecasting tasks are usually conducted in a channel-dependent (CD) way since it can incorporate more variable-relevant information. However, it may also involve a lot of irrelevant variables, an…
ClusteringCollaborative FilteringGraph ClusteringMultivariate Time Series Forecasting+2A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methods
Time series forecasting has gained lots of attention recently; this is because many real-world phenomena can be modeled as time series. The massive volume of data and recent advancements in the processing power of the co…
Anomaly DetectionClusteringTime SeriesTime Series Analysis+1