# Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1509.07481 · HTML: https://mlsanity.com/paper/1509.07481/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2015.

## Built on by (most cited first, 8 of 8)

- [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)
- [VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbones](https://mlsanity.com/paper/2508.04379/) (2025)
- [AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification](https://mlsanity.com/paper/2504.09993/) (2025)
- [Spectral Propagation Graph Network for Few-shot Time Series Classification](https://mlsanity.com/paper/2202.04769/) (2022)
- [Image-based Data Representations of Time Series: A Comparative Analysis in EEG Artifact Detection](https://mlsanity.com/paper/2401.05409/) (2023)
- [Deep Learning-Based Anomaly Detection in Spacecraft Telemetry on Edge Devices](https://mlsanity.com/paper/2603.29375/) (2026)
- [Phase-OTDR Event Detection Using Image-Based Data Transformation and Deep Learning](https://mlsanity.com/paper/2512.05830/) (2025)

## Abstract (arXiv)

We propose an off-line approach to explicitly encode temporal patterns spatially as different types of images, namely, Gramian Angular Fields and Markov Transition Fields. This enables the use of techniques from computer vision for feature learning and classification. We used Tiled Convolutional Neural Networks to learn high-level features from individual GAF, MTF, and GAF-MTF images on 12 benchmark time series datasets and two real spatial-temporal trajectory datasets. The classification results of our approach are competitive with state-of-the-art approaches on both types of data. An analysis of the features and weights learned by the CNNs explains why the approach works.