# Multivariate Time Series Classification using Dilated Convolutional Neural Network

arXiv: https://arxiv.org/abs/1905.01697 · HTML: https://mlsanity.com/paper/1905.01697/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2019.

## Built on by (most cited first, 9 of 9)

- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [Harnessing Vision Models for Time Series Analysis: A Survey](https://mlsanity.com/paper/2502.08869/) (2025)
- [L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing](https://mlsanity.com/paper/2104.05499/) (2021)
- [TimEHR: Image-based Time Series Generation for Electronic Health Records](https://mlsanity.com/paper/2402.06318/) (2024)
- [Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms](https://mlsanity.com/paper/2310.02812/) (2023)
- [Short term prediction of demand for ride hailing services: A deep learning approach](https://mlsanity.com/paper/2212.03956/) (2022)
- [Dilated filters for edge detection algorithms](https://mlsanity.com/paper/2106.07395/) (2021)
- [Optimizing Hyperparameters in CNNs using Bilevel Programming in Time Series Data](https://mlsanity.com/paper/2101.07492/) (2021)
- [Fuzzy-Logic and Deep Learning for Environmental Condition-Aware Road Surface Classification](https://mlsanity.com/paper/2512.23436/) (2025)

## Abstract (arXiv)

Multivariate time series classification is a high value and well-known problem in machine learning community. Feature extraction is a main step in classification tasks. Traditional approaches employ hand-crafted features for classification while convolutional neural networks (CNN) are able to extract features automatically. In this paper, we use dilated convolutional neural network for multivariate time series classification. To deploy dilated CNN, a multivariate time series is transformed into an image-like style and stacks of dilated and strided convolutions are applied to extract in and between features of variates in time series simultaneously. We evaluate our model on two human activity recognition time series, finding that the automatic features extracted for the time series can be as effective as hand-crafted features.