Timage -- A Robust Time Series Classification Pipeline

TL;DR

This paper introduces Timage, a novel pipeline for time series classification that leverages transfer learning with ResNet on Recurrence Plot representations, simplifying preprocessing and enabling multi-dataset training.

Contribution

It proposes a new method for multi-time series classification using a single neural network trained across multiple datasets, utilizing 2D Recurrence Plots and transfer learning.

Findings

Effective classification on UCR archive datasets

Simplified preprocessing with few parameters

First to evaluate multi-dataset training with ResNet

Abstract

Time series are series of values ordered by time. This kind of data can be found in many real world settings. Classifying time series is a difficult task and an active area of research. This paper investigates the use of transfer learning in Deep Neural Networks and a 2D representation of time series known as Recurrence Plots. In order to utilize the research done in the area of image classification, where Deep Neural Networks have achieved very good results, we use a Residual Neural Networks architecture known as ResNet. As preprocessing of time series is a major part of every time series classification pipeline, the method proposed simplifies this step and requires only few parameters. For the first time we propose a method for multi time series classification: Training a single network to classify all datasets in the archive with one network. We are among the first to evaluate the method on the latest 2018 release of the UCR archive, a well established time series classification benchmarking dataset.