# TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity

arXiv: https://arxiv.org/abs/2608.08119 · HTML: https://mlsanity.com/paper/2608.08119/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 19 of 19)

- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://mlsanity.com/paper/2409.16040/) (2024)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Deep Time Series Models: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2407.13278/) (2024)
- [Geometric Dataset Distances via Optimal Transport](https://mlsanity.com/paper/2002.02923/) (2020)
- [UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting](https://mlsanity.com/paper/2310.09751/) (2023)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [Retrieval Augmented Time Series Forecasting](https://mlsanity.com/paper/2411.08249/) (2024)
- [Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment](https://mlsanity.com/paper/2308.12686/) (2023)
- [The impact of data set similarity and diversity on transfer learning success in time series forecasting](https://mlsanity.com/paper/2404.06198/) (2024)
- [Estimating Time Series Foundation Model Transferability via In-Context Learning](https://mlsanity.com/paper/2509.23695/) (2025)
- [Measuring Time-Series Dataset Similarity using Wasserstein Distance](https://mlsanity.com/paper/2507.22189/) (2025)

## Abstract (arXiv)

The rapid advancement of artificial intelligence (AI) has significantly accelerated research in time-series analysis, particularly in forecasting, classification, and generation tasks. Recent models, especially foundation models, benefit from time-series dataset similarity due to its significant role in source dataset selection for fine-tuning. However, many existing implementations for benchmarking time-series dataset similarity methods are fragmented and difficult to extend. To address this, we present a unified framework, the Time-Series Dataset Similarity Toolbox (TSDS-Toolbox). Our work enables (1) systematic and reproducible comparisons of time-series dataset similarity methods; (2) flexible extensibility for users to add customized datasets, similarity methods, and downstream time-series tasks; and (3) consistent evaluation of both dataset-level and series-level similarity methods through integrated time-series dataset reducers. The effectiveness of TSDS-Toolbox is validated through comprehensive experiments under diverse experimental settings. Our toolbox is publicly available.