# Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

arXiv: https://arxiv.org/abs/2409.14327 · HTML: https://mlsanity.com/paper/2409.14327/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 22 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Transformers in Time-series Analysis: A Tutorial](https://mlsanity.com/paper/2205.01138/) (2022)
- [Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository](https://mlsanity.com/paper/1809.06186/) (2018)

## Built on by (most cited first, 30 of 47)

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- [Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data](https://mlsanity.com/paper/2502.03803/) (2025)
- [Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs](https://mlsanity.com/paper/2411.15195/) (2024)
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- [Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining](https://mlsanity.com/paper/2412.15593/) (2024)
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- [Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data](https://mlsanity.com/paper/2411.18622/) (2024)
- [Accurate Medical Named Entity Recognition Through Specialized NLP Models](https://mlsanity.com/paper/2412.08255/) (2024)
- [Context-Aware Rule Mining Using a Dynamic Transformer-Based Framework](https://mlsanity.com/paper/2503.11125/) (2025)
- [A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention](https://mlsanity.com/paper/2504.15223/) (2025)
- [Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management](https://mlsanity.com/paper/2410.19211/) (2024)
- [Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models](https://mlsanity.com/paper/2412.03606/) (2024)
- [Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision](https://mlsanity.com/paper/2412.15222/) (2024)
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- [Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining](https://mlsanity.com/paper/2504.04032/) (2025)
- [Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQN](https://mlsanity.com/paper/2504.09344/) (2025)
- [Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation](https://mlsanity.com/paper/2505.07674/) (2025)

## Abstract (arXiv)

This paper introduces a novel spatiotemporal feature representation model designed to address the limitations of traditional methods in multidimensional time series (MTS) analysis. The proposed approach converts MTS into one-dimensional sequences of spatially evolving events, preserving the complex coupling relationships between dimensions. By employing a variable-length tuple mining method, key spatiotemporal features are extracted, enhancing the interpretability and accuracy of time series analysis. Unlike conventional models, this unsupervised method does not rely on large training datasets, making it adaptable across different domains. Experimental results from motion sequence classification validate the model's superior performance in capturing intricate patterns within the data. The proposed framework has significant potential for applications across various fields, including backend services for monitoring and optimizing IT infrastructure, medical diagnosis through continuous patient monitoring and health trend analysis, and internet businesses for tracking user behavior and forecasting sales. This work offers a new theoretical foundation and technical support for advancing time series data mining and its practical applications in human behavior recognition and other domains.