# Image Transformation for IoT Time-Series Data: A Review

arXiv: https://arxiv.org/abs/2311.12742 · HTML: https://mlsanity.com/paper/2311.12742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 92 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Classification of Time-Series Images Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1710.00886/) (2017)
- [Classification of Hyperspectral and LiDAR Data Using Coupled CNNs](https://mlsanity.com/paper/2002.01144/) (2020)
- [Time series compression: a survey](https://mlsanity.com/paper/2101.08784/) (2021)
- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images](https://mlsanity.com/paper/2005.04188/) (2020)
- [Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies](https://mlsanity.com/paper/2104.00972/) (2021)
- [Deep Image: A precious image based deep learning method for online malware detection in IoT Environment](https://mlsanity.com/paper/2204.01690/) (2022)

## Abstract (arXiv)

In the era of the Internet of Things (IoT), where smartphones, built-in systems, wireless sensors, and nearly every smart device connect through local networks or the internet, billions of smart things communicate with each other and generate vast amounts of time-series data. As IoT time-series data is high-dimensional and high-frequency, time-series classification or regression has been a challenging issue in IoT. Recently, deep learning algorithms have demonstrated superior performance results in time-series data classification in many smart and intelligent IoT applications. However, it is hard to explore the hidden dynamic patterns and trends in time-series. Recent studies show that transforming IoT data into images improves the performance of the learning model. In this paper, we present a review of these studies which use image transformation/encoding techniques in IoT domain. We examine the studies according to their encoding techniques, data types, and application areas. Lastly, we emphasize the challenges and future dimensions of image transformation.