# GLA-DA: Global-Local Alignment Domain Adaptation for Multivariate Time Series

arXiv: https://arxiv.org/abs/2410.06671 · HTML: https://mlsanity.com/paper/2410.06671/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data](https://mlsanity.com/paper/2005.10996/) (2020)
- [Active Adversarial Domain Adaptation](https://mlsanity.com/paper/1904.07848/) (2019)
- [Category Contrast for Unsupervised Domain Adaptation in Visual Tasks](https://mlsanity.com/paper/2106.02885/) (2021)
- [Time Series Domain Adaptation via Sparse Associative Structure Alignment](https://mlsanity.com/paper/2012.11797/) (2020)
- [ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data](https://mlsanity.com/paper/2203.08321/) (2022)
- [Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification](https://mlsanity.com/paper/2208.06616/) (2022)
- [Self-supervised Autoregressive Domain Adaptation for Time Series Data](https://mlsanity.com/paper/2111.14834/) (2021)
- [Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation](https://mlsanity.com/paper/2008.01677/) (2020)
- [HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks](https://mlsanity.com/paper/2210.08884/) (2022)
- [Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation](https://mlsanity.com/paper/1912.00320/) (2019)
- [Context-aware Domain Adaptation for Time Series Anomaly Detection](https://mlsanity.com/paper/2304.07453/) (2023)
- [Metric-Learning-Assisted Domain Adaptation](https://mlsanity.com/paper/2004.10963/) (2020)
- [Semi-Supervised Domain Adaptation by Similarity based Pseudo-label Injection](https://mlsanity.com/paper/2209.01881/) (2022)

## Built on by (most cited first, 2 of 2)

- [SemiHMER: Semi-supervised Handwritten Mathematical Expression Recognition using pseudo-labels](https://mlsanity.com/paper/2502.07172/) (2025)
- [Continuous Cross-Domain Traffic State Prediction via Memory-Augmented Graph Liquid Time-Constant Networks](https://mlsanity.com/paper/2606.15807/) (2026)

## Abstract (arXiv)

Unlike images and natural language tokens, time series data is highly semantically sparse, resulting in labor-intensive label annotations. Unsupervised and Semi-supervised Domain Adaptation (UDA and SSDA) have demonstrated efficiency in addressing this issue by utilizing pre-labeled source data to train on unlabeled or partially labeled target data. However, in domain adaptation methods designed for downstream classification tasks, directly adapting labeled source samples with unlabelled target samples often results in similar distributions across various classes, thereby compromising the performance of the target classification task. To tackle this challenge, we proposed a Global-Local Alignment Domain Adaptation (GLA-DA) method for multivariate time series data. Data from two domains were initially encoded to align in an intermediate feature space adversarially, achieving Global Feature Alignment (GFA). Subsequently, GLA-DA leveraged the consistency between similarity-based and deep learning-based models to assign pseudo labels to unlabeled target data. This process aims to preserve differences among data with distinct labels by aligning the samples with the same class labels together, achieving Local Class Alignment (LCA). We implemented GLA-DA in both UDA and SSDA scenarios, showcasing its superiority over state-of-the-art methods through extensive experiments on various public datasets. Ablation experiments underscored the significance of key components within GLA-DA.