# Classification of Time-Series Images Using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1710.00886 · HTML: https://mlsanity.com/paper/1710.00886/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2017.

## Built on by (most cited first, 30 of 38)

- [VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2408.17253/) (2024)
- [Time Series as Images: Vision Transformer for Irregularly Sampled Time Series](https://mlsanity.com/paper/2303.12799/) (2023)
- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series](https://mlsanity.com/paper/2410.19538/) (2024)
- [Harnessing Vision Models for Time Series Analysis: A Survey](https://mlsanity.com/paper/2502.08869/) (2025)
- [A Novel CNN-LSTM-based Approach to Predict Urban Expansion](https://mlsanity.com/paper/2103.01695/) (2021)
- [VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbones](https://mlsanity.com/paper/2508.04379/) (2025)
- [TimEHR: Image-based Time Series Generation for Electronic Health Records](https://mlsanity.com/paper/2402.06318/) (2024)
- [A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Masking](https://mlsanity.com/paper/2510.06699/) (2025)
- [Fully Homomorphically Encrypted Deep Learning as a Service](https://mlsanity.com/paper/2107.12997/) (2021)
- [AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification](https://mlsanity.com/paper/2504.09993/) (2025)
- [How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation](https://mlsanity.com/paper/2407.08442/) (2024)
- [Channel Selected Stratified Nested Cross Validation for Clinically Relevant EEG Based Parkinsons Disease Detection](https://mlsanity.com/paper/2601.05276/) (2025)
- [Semi-supervised multiscale dual-encoding method for faulty traffic data detection](https://mlsanity.com/paper/2212.13596/) (2022)
- [MCI Detection using fMRI time series embeddings of Recurrence plots](https://mlsanity.com/paper/2311.18265/) (2023)
- [Spectral Propagation Graph Network for Few-shot Time Series Classification](https://mlsanity.com/paper/2202.04769/) (2022)
- [Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification: A survey](https://mlsanity.com/paper/2204.08461/) (2022)
- [Sensor-Aware Classifiers for Energy-Efficient Time Series Applications on IoT Devices](https://mlsanity.com/paper/2407.08715/) (2024)
- [Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality](https://mlsanity.com/paper/2507.23253/) (2025)
- [A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network](https://mlsanity.com/paper/2101.00702/) (2021)
- [Image Classifiers for Network Intrusions](https://mlsanity.com/paper/2103.07765/) (2021)
- [Pre-Trained Language Transformers are Universal Image Classifiers](https://mlsanity.com/paper/2201.10182/) (2022)
- [Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report](https://mlsanity.com/paper/2304.06286/) (2023)
- [Multi-step feature fusion for natural disaster damage assessment on satellite images](https://mlsanity.com/paper/2410.21901/) (2024)
- [Integrating Sequence and Image Modeling in Irregular Medical Time Series Through Self-Supervised Learning](https://mlsanity.com/paper/2502.06134/) (2025)
- [Moon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2510.01970/) (2025)
- [VTBench: A Multimodal Framework for Time-Series Classification with Chart-Based Representations](https://mlsanity.com/paper/2604.27259/) (2026)
- [Deep Neural Networks on EEG Signals to Predict Auditory Attention Score Using Gramian Angular Difference Field](https://mlsanity.com/paper/2110.12503/) (2021)
- [A Dynamic Temporal Self-attention Graph Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2302.10428/) (2023)
- [Image Transformation for IoT Time-Series Data: A Review](https://mlsanity.com/paper/2311.12742/) (2023)

## Abstract (arXiv)

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.