# Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification

arXiv: https://arxiv.org/abs/2303.17809 · HTML: https://mlsanity.com/paper/2303.17809/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [Highly comparative feature-based time-series classification](https://mlsanity.com/paper/1401.3531/) (2014)
- [Highly comparative time-series analysis: The empirical structure of time series and their methods](https://mlsanity.com/paper/1304.1209/) (2013)
- [Feature-based time-series analysis](https://mlsanity.com/paper/1709.08055/) (2017)
- [When is Early Classification of Time Series Meaningful?](https://mlsanity.com/paper/2102.11487/) (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)
- [Feature-Based Time-Series Analysis in R using the theft Package](https://mlsanity.com/paper/2208.06146/) (2022)
- [Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands](https://mlsanity.com/paper/2007.03924/) (2020)

## Built on by (most cited first, 4 of 4)

- [QUANT: A Minimalist Interval Method for Time Series Classification](https://mlsanity.com/paper/2308.00928/) (2023)
- [Revisit Time Series Classification Benchmark: The Impact of Temporal Information for Classification](https://mlsanity.com/paper/2503.20264/) (2025)
- [Parameter inference from a non-stationary unknown process](https://mlsanity.com/paper/2407.08987/) (2024)
- [Statistical comparisons of time-series feature sets on classification tasks](https://mlsanity.com/paper/2608.01586/) (2026)

## Abstract (arXiv)

The variety of complex algorithmic approaches for tackling time-series classification problems has grown considerably over the past decades, including the development of sophisticated but challenging-to-interpret deep-learning-based methods. But without comparison to simpler methods it can be difficult to determine when such complexity is required to obtain strong performance on a given problem. Here we evaluate the performance of an extremely simple classification approach -- a linear classifier in the space of two simple features that ignore the sequential ordering of the data: the mean and standard deviation of time-series values. Across a large repository of 128 univariate time-series classification problems, this simple distributional moment-based approach outperformed chance on 69 problems, and reached 100% accuracy on two problems. With a neuroimaging time-series case study, we find that a simple linear model based on the mean and standard deviation performs better at classifying individuals with schizophrenia than a model that additionally includes features of the time-series dynamics. Comparing the performance of simple distributional features of a time series provides important context for interpreting the performance of complex time-series classification models, which may not always be required to obtain high accuracy.