# Plotting time: On the usage of CNNs for time series classification

arXiv: https://arxiv.org/abs/2102.04179 · HTML: https://mlsanity.com/paper/2102.04179/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification](https://mlsanity.com/paper/1906.10329/) (2019)
- [Proximity Forest: An effective and scalable distance-based classifier for time series](https://mlsanity.com/paper/1808.10594/) (2018)
- [From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers](https://mlsanity.com/paper/1809.06751/) (2018)

## Built on by (most cited first, 3 of 3)

- [VTBench: A Multimodal Framework for Time-Series Classification with Chart-Based Representations](https://mlsanity.com/paper/2604.27259/) (2026)
- [Deep Learning Image Recognition for Non-images](https://mlsanity.com/paper/2106.14350/) (2021)
- [PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection](https://mlsanity.com/paper/2608.03926/) (2026)

## Abstract (arXiv)

We present a novel approach for time series classification where we represent time series data as plot images and feed them to a simple CNN, outperforming several state-of-the-art methods. We propose a simple and highly replicable way of plotting the time series, and feed these images as input to a non-optimized shallow CNN, without any normalization or residual connections. These representations are no more than default line plots using the time series data, where the only pre-processing applied is to reduce the number of white pixels in the image. We compare our method with different state-of-the-art methods specialized in time series classification on two real-world non public datasets, as well as 98 datasets of the UCR dataset collection. The results show that our approach is very promising, achieving the best results on both real-world datasets and matching / beating the best state-of-the-art methods in six UCR datasets. We argue that, if a simple naive design like ours can obtain such good results, it is worth further exploring the capabilities of using image representation of time series data, along with more powerful CNNs, for classification and other related tasks.