ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks

The challenge of effectively learning inter-series correlations for multivariate time series forecasting remains a substantial and unresolved problem. Traditional deep learning models, which are largely dependent on the Transformer paradigm for modeling long sequences, often fail to integrate information from multiple time series into a coherent and universally applicable model. To bridge this gap, our paper presents ForecastGrapher, a framework reconceptualizes multivariate time series forecasting as a node regression task, providing a unique avenue for capturing the intricate temporal dynamics and inter-series correlations. Our approach is underpinned by three pivotal steps: firstly, generating custom node embeddings to reflect the temporal variations within each series; secondly, constructing an adaptive adjacency matrix to encode the inter-series correlations; and thirdly, augmenting the GNNs' expressive power by diversifying the node feature distribution. To enhance this expressive power, we introduce the Group Feature Convolution GNN (GFC-GNN). This model employs a learnable scaler to segment node features into multiple groups and applies one-dimensional convolutions with different kernel lengths to each group prior to the aggregation phase. Consequently, the GFC-GNN method enriches the diversity of node feature distribution in a fully end-to-end fashion. Through extensive experiments and ablation studies, we show that ForecastGrapher surpasses strong baselines and leading published techniques in the domain of multivariate time series forecasting.

NuwaTS: a Foundation Model Mending Every Incomplete Time Series

Time series imputation plays a crucial role in various real-world systems and has been extensively explored. Models for time series imputation often require specialization, necessitating distinct designs for different domains and missing patterns. In this study, we introduce NuwaTS, a framework to repurpose Pre-trained Language Model (PLM) for general time series imputation. Once trained, this model can be applied to imputation tasks on incomplete time series from any domain with any missing patterns. We begin by devising specific embeddings for each sub-series patch of the incomplete time series. These embeddings encapsulate information about the patch itself, the missing data patterns within the patch, and the patch's statistical characteristics. To enhance the model's adaptability to different missing patterns, we propose a contrastive learning approach to make representations of the same patch more similar across different missing patterns. By combining this contrastive loss with the missing data imputation task, we train PLMs to obtain a one-for-all imputation model. Furthermore, we utilize a plug-and-play layer-wise fine-tuning approach to train domain-specific models. Experimental results demonstrate that leveraging a dataset of over seventeen million time series from diverse domains, we obtain a one-for-all imputation model which outperforms existing domain-specific models across various datasets and missing patterns. Additionally, we find that NuwaTS can be generalized to other time series tasks such as forecasting. Our codes are available at https://github.com/Chengyui/NuwaTS.

MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting

Multivariate time series forecasting poses an ongoing challenge across various disciplines. Time series data often exhibit diverse intra-series and inter-series correlations, contributing to intricate and interwoven dependencies that have been the focus of numerous studies. Nevertheless, a significant research gap remains in comprehending the varying inter-series correlations across different time scales among multiple time series, an area that has received limited attention in the literature. To bridge this gap, this paper introduces MSGNet, an advanced deep learning model designed to capture the varying inter-series correlations across multiple time scales using frequency domain analysis and adaptive graph convolution. By leveraging frequency domain analysis, MSGNet effectively extracts salient periodic patterns and decomposes the time series into distinct time scales. The model incorporates a self-attention mechanism to capture intra-series dependencies, while introducing an adaptive mixhop graph convolution layer to autonomously learn diverse inter-series correlations within each time scale. Extensive experiments are conducted on several real-world datasets to showcase the effectiveness of MSGNet. Furthermore, MSGNet possesses the ability to automatically learn explainable multi-scale inter-series correlations, exhibiting strong generalization capabilities even when applied to out-of-distribution samples.