# Time series classification for varying length series

arXiv: https://arxiv.org/abs/1910.04341 · HTML: https://mlsanity.com/paper/1910.04341/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data](https://mlsanity.com/paper/2402.14989/) (2024)
- [ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging](https://mlsanity.com/paper/2309.16353/) (2023)
- [Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series](https://mlsanity.com/paper/2304.05800/) (2023)
- [DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis](https://mlsanity.com/paper/2401.04979/) (2024)
- [PTLp: Partial Transport Lp Distances](https://mlsanity.com/paper/2307.13571/) (2023)
- [Operational Learning-based Boundary Estimation in Electromagnetic Medical Imaging](https://mlsanity.com/paper/2108.03233/) (2021)
- [On Mini-Batch Training with Varying Length Time Series](https://mlsanity.com/paper/2212.06536/) (2022)
- [Approximating DTW with a convolutional neural network on EEG data](https://mlsanity.com/paper/2301.12873/) (2023)
- [Unobserved classes and extra variables in high-dimensional discriminant analysis](https://mlsanity.com/paper/2102.01982/) (2021)
- [ASTRIDE: Adaptive Symbolization for Time Series Databases](https://mlsanity.com/paper/2302.04097/) (2023)

## Abstract (arXiv)

Research into time series classification has tended to focus on the case of series of uniform length. However, it is common for real-world time series data to have unequal lengths. Differing time series lengths may arise from a number of fundamentally different mechanisms. In this work, we identify and evaluate two classes of such mechanisms -- variations in sampling rate relative to the relevant signal and variations between the start and end points of one time series relative to one another. We investigate how time series generated by each of these classes of mechanism are best addressed for time series classification. We perform extensive experiments and provide practical recommendations on how variations in length should be handled in time series classification.