TimeMKG: Knowledge-Infused Causal Reasoning for Multivariate Time Series Modeling

• URL: http://arxiv.org/abs/2508.09630v2
• Date: Fri, 15 Aug 2025 05:09:03 GMT
• Title: TimeMKG: Knowledge-Infused Causal Reasoning for Multivariate Time Series Modeling
• Authors: Yifei Sun, Junming Liu, Yirong Chen, Xuefeng Yan, Ding Wang,
• Abstract summary: TimeMKG elevates time series modeling from low-level signal processing to knowledge informed inference.<n>Cross-modality attention aligns representations at the variable level, injecting causal priors into downstream tasks.<n>Experiment in diverse datasets demonstrates that incorporating variable-level knowledge significantly improves both predictive performance and generalization.
• Score: 19.07644799215496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate time series data typically comprises two distinct modalities: variable semantics and sampled numerical observations. Traditional time series models treat variables as anonymous statistical signals, overlooking the rich semantic information embedded in variable names and data descriptions. However, these textual descriptors often encode critical domain knowledge that is essential for robust and interpretable modeling. Here we present TimeMKG, a multimodal causal reasoning framework that elevates time series modeling from low-level signal processing to knowledge informed inference. TimeMKG employs large language models to interpret variable semantics and constructs structured Multivariate Knowledge Graphs that capture inter-variable relationships. A dual-modality encoder separately models the semantic prompts, generated from knowledge graph triplets, and the statistical patterns from historical time series. Cross-modality attention aligns and fuses these representations at the variable level, injecting causal priors into downstream tasks such as forecasting and classification, providing explicit and interpretable priors to guide model reasoning. The experiment in diverse datasets demonstrates that incorporating variable-level knowledge significantly improves both predictive performance and generalization.

Related papers

• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• TimesBERT: A BERT-Style Foundation Model for Time Series Understanding [72.64824086839631]
  GPT-style models have been positioned as foundation models for time series forecasting.<n>BERT-style architecture has not been fully unlocked for time series understanding.<n>We design TimesBERT to learn generic representations of time series.<n>Our model is pre-trained on 260 billion time points across diverse domains.
  arXiv  Detail & Related papers  (2025-02-28T17:14:44Z)
• Metadata Matters for Time Series: Informative Forecasting with Transformers [70.38241681764738]
  We propose a Metadata-informed Time Series Transformer (MetaTST) for time series forecasting. To tackle the unstructured nature of metadata, MetaTST formalizes them into natural languages by pre-designed templates. A Transformer encoder is employed to communicate series and metadata tokens, which can extend series representations by metadata information.
  arXiv  Detail & Related papers  (2024-10-04T11:37:55Z)
• Ti-MAE: Self-Supervised Masked Time Series Autoencoders [16.98069693152999]
  We propose a novel framework named Ti-MAE, in which the input time series are assumed to follow an integrate distribution. Ti-MAE randomly masks out embedded time series data and learns an autoencoder to reconstruct them at the point-level. Experiments on several public real-world datasets demonstrate that our framework of masked autoencoding could learn strong representations directly from the raw data.
  arXiv  Detail & Related papers  (2023-01-21T03:20:23Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification [0.0]
  Multivariable time series classification problems are increasing in prevalence and complexity. Deep learning methods are an effective tool for these problems, but they often lack interpretability. We propose a novel modular prototype learning framework for multivariable time series classification.
  arXiv  Detail & Related papers  (2021-06-17T16:32:47Z)
• Model-Attentive Ensemble Learning for Sequence Modeling [86.4785354333566]
  We present Model-Attentive Ensemble learning for Sequence modeling (MAES) MAES is a mixture of time-series experts which leverages an attention-based gating mechanism to specialize the experts on different sequence dynamics and adaptively weight their predictions. We demonstrate that MAES significantly out-performs popular sequence models on datasets subject to temporal shift.
  arXiv  Detail & Related papers  (2021-02-23T05:23:35Z)
• Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals [32.30627405832656]
  Time series data is prevalent in a wide variety of real-world applications. Key criterion to understand such predictive models involves elucidating and quantifying the contribution of time varying input variables to the classification. We introduce a novel, modular, convolution-based feature extraction and attention mechanism that simultaneously identifies the variables as well as time intervals which determine the classification output.
  arXiv  Detail & Related papers  (2020-11-23T19:16:46Z)
• Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation [17.568599402858037]
  temporal attention mechanism (STAM) for simultaneous learning of the most important time steps and variables. Results: STAM maintains state-of-the-art prediction accuracy while offering the benefit of accurate interpretability.
  arXiv  Detail & Related papers  (2020-08-11T17:34:55Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)

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.