# Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations

arXiv: https://arxiv.org/abs/2006.01667 · HTML: https://mlsanity.com/paper/2006.01667/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 30 of 31)

- [HIVE-COTE 2.0: a new meta ensemble for time series classification](https://mlsanity.com/paper/2104.07551/) (2021)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey](https://mlsanity.com/paper/2104.00950/) (2021)
- [HYDRA: Competing convolutional kernels for fast and accurate time series classification](https://mlsanity.com/paper/2203.13652/) (2022)
- [Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets](https://mlsanity.com/paper/2109.13514/) (2021)
- [Concept Learning for Interpretable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.12232/) (2023)
- [Interpretation of Time-Series Deep Models: A Survey](https://mlsanity.com/paper/2305.14582/) (2023)
- [Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification](https://mlsanity.com/paper/2206.09274/) (2022)
- [Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields](https://mlsanity.com/paper/2311.18029/) (2023)
- [MrSQM: Fast Time Series Classification with Symbolic Representations](https://mlsanity.com/paper/2109.01036/) (2021)
- [Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring](https://mlsanity.com/paper/2207.07753/) (2022)
- [Robust Explainer Recommendation for Time Series Classification](https://mlsanity.com/paper/2306.05501/) (2023)
- [Fast, Accurate and Interpretable Time Series Classification Through Randomization](https://mlsanity.com/paper/2105.14876/) (2021)
- [Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series](https://mlsanity.com/paper/2304.05800/) (2023)
- [Retrieval-Augmented Mining of Temporal Logic Specifications from Data](https://mlsanity.com/paper/2405.14355/) (2024)
- [Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification](https://mlsanity.com/paper/2406.12507/) (2024)
- [The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification](https://mlsanity.com/paper/2603.20352/) (2026)
- [RED CoMETS: An ensemble classifier for symbolically represented multivariate time series](https://mlsanity.com/paper/2307.13679/) (2023)
- [Evaluating Explanation Methods for Multivariate Time Series Classification](https://mlsanity.com/paper/2308.15223/) (2023)
- [Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic Systems](https://mlsanity.com/paper/2104.02784/) (2021)
- [ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts](https://mlsanity.com/paper/2511.02152/) (2025)
- [Pattern Sampling for Shapelet-based Time Series Classification](https://mlsanity.com/paper/2102.08498/) (2021)
- [COSTI: a New Classifier for Sequences of Temporal Intervals](https://mlsanity.com/paper/2204.13467/) (2022)
- [Automated Mobility Context Detection with Inertial Signals](https://mlsanity.com/paper/2205.08409/) (2022)
- [ASTRIDE: Adaptive Symbolization for Time Series Databases](https://mlsanity.com/paper/2302.04097/) (2023)
- [Interpretable time series neural representation for classification purposes](https://mlsanity.com/paper/2310.16696/) (2023)
- [Castor: Competing shapelets for fast and accurate time series classification](https://mlsanity.com/paper/2403.13176/) (2024)
- [Classification of High-dimensional Time Series in Spectral Domain using Explainable Features](https://mlsanity.com/paper/2408.08388/) (2024)
- [ST-Tree with Interpretability for Multivariate Time Series Classification](https://mlsanity.com/paper/2411.11620/) (2024)
- [Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semantics](https://mlsanity.com/paper/2511.04244/) (2025)

## Abstract (arXiv)

The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. Prior research has mostly focused on improving the accuracy and efficiency of classifiers, with interpretability being somewhat neglected. This aspect of classifiers has become critical for many application domains and the introduction of the EU GDPR legislation in 2018 is likely to further emphasize the importance of interpretable learning algorithms. Currently, state-of-the-art classification accuracy is achieved with very complex models based on large ensembles (COTE) or deep neural networks (FCN). These approaches are not efficient with regard to either time or space, are difficult to interpret and cannot be applied to variable-length time series, requiring pre-processing of the original series to a set fixed-length. In this paper we propose new time series classification algorithms to address these gaps. Our approach is based on symbolic representations of time series, efficient sequence mining algorithms and linear classification models. Our linear models are as accurate as deep learning models but are more efficient regarding running time and memory, can work with variable-length time series and can be interpreted by highlighting the discriminative symbolic features on the original time series. We show that our multi-resolution multi-domain linear classifier (mtSS-SEQL+LR) achieves a similar accuracy to the state-of-the-art COTE ensemble, and to recent deep learning methods (FCN, ResNet), but uses a fraction of the time and memory required by either COTE or deep models. To further analyse the interpretability of our classifier, we present a case study on a human motion dataset collected by the authors. We release all the results, source code and data to encourage reproducibility.