# Dynamic Multi-Network Mining of Tensor Time Series

arXiv: https://arxiv.org/abs/2402.11773 · HTML: https://mlsanity.com/paper/2402.11773/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting](https://mlsanity.com/paper/1905.10069/) (2019)
- [Network Inference via the Time-Varying Graphical Lasso](https://mlsanity.com/paper/1703.01958/) (2017)
- [Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data](https://mlsanity.com/paper/1706.03161/) (2017)
- [Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases](https://mlsanity.com/paper/2007.03988/) (2020)
- [Network of Tensor Time Series](https://mlsanity.com/paper/2102.07736/) (2021)
- [Node-Based Learning of Multiple Gaussian Graphical Models](https://mlsanity.com/paper/1303.5145/) (2013)
- [Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series](https://mlsanity.com/paper/1310.3863/) (2013)
- [SSMF: Shifting Seasonal Matrix Factorization](https://mlsanity.com/paper/2110.12763/) (2021)
- [Fast and Multi-aspect Mining of Complex Time-stamped Event Streams](https://mlsanity.com/paper/2303.03789/) (2023)
- [Latent Variable Time-varying Network Inference](https://mlsanity.com/paper/1802.03987/) (2018)
- [Tensor Analysis with n-Mode Generalized Difference Subspace](https://mlsanity.com/paper/1909.01954/) (2019)
- [Efficient Covariance Estimation from Temporal Data](https://mlsanity.com/paper/1905.13276/) (2019)

## Built on by (most cited first, 8 of 8)

- [EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis](https://mlsanity.com/paper/2602.02551/) (2026)
- [Long-Term EEG Partitioning for Seizure Onset Detection](https://mlsanity.com/paper/2412.15598/) (2024)
- [Modeling Time-evolving Causality over Data Streams](https://mlsanity.com/paper/2502.08963/) (2025)
- [Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series](https://mlsanity.com/paper/2409.09930/) (2024)
- [Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams](https://mlsanity.com/paper/2601.04741/) (2026)
- [KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting](https://mlsanity.com/paper/2510.02084/) (2025)
- [Interpretable Dynamic Network Modeling of Tensor Time Series via Kronecker Time-Varying Graphical Lasso](https://mlsanity.com/paper/2602.08197/) (2026)
- [Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning](https://mlsanity.com/paper/2602.23663/) (2026)

## Abstract (arXiv)

Subsequence clustering of time series is an essential task in data mining, and interpreting the resulting clusters is also crucial since we generally do not have prior knowledge of the data. Thus, given a large collection of tensor time series consisting of multiple modes, including timestamps, how can we achieve subsequence clustering for tensor time series and provide interpretable insights? In this paper, we propose a new method, Dynamic Multi-network Mining (DMM), that converts a tensor time series into a set of segment groups of various lengths (i.e., clusters) characterized by a dependency network constrained with l1-norm. Our method has the following properties. (a) Interpretable: it characterizes the cluster with multiple networks, each of which is a sparse dependency network of a corresponding non-temporal mode, and thus provides visible and interpretable insights into the key relationships. (b) Accurate: it discovers the clusters with distinct networks from tensor time series according to the minimum description length (MDL). (c) Scalable: it scales linearly in terms of the input data size when solving a non-convex problem to optimize the number of segments and clusters, and thus it is applicable to long-range and high-dimensional tensors. Extensive experiments with synthetic datasets confirm that our method outperforms the state-of-the-art methods in terms of clustering accuracy. We then use real datasets to demonstrate that DMM is useful for providing interpretable insights from tensor time series.