The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier

TL;DR

The paper introduces FreshPRINCE, a simple yet effective time series classification pipeline using feature extraction and rotation forest, providing a strong benchmark that outperforms traditional methods like DTW.

Contribution

It demonstrates that a straightforward feature extraction pipeline with rotation forest can achieve competitive accuracy, challenging the necessity of complex algorithms in TSC.

Findings

FreshPRINCE outperforms DTW-based nearest neighbor classifiers.

Simple feature extraction pipelines can serve as strong benchmarks.

The approach is not state-of-the-art but offers a practical, effective alternative.

Abstract

There have recently been significant advances in the accuracy of algorithms proposed for time series classification (TSC). However, a commonly asked question by real world practitioners and data scientists less familiar with the research topic, is whether the complexity of the algorithms considered state of the art is really necessary. Many times the first approach suggested is a simple pipeline of summary statistics or other time series feature extraction approaches such as TSFresh, which in itself is a sensible question; in publications on TSC algorithms generalised for multiple problem types, we rarely see these approaches considered or compared against. We experiment with basic feature extractors using vector based classifiers shown to be effective with continuous attributes in current state-of-the-art time series classifiers. We test these approaches on the UCR time series dataset archive, looking to see if TSC literature has overlooked the effectiveness of these approaches. We find that a pipeline of TSFresh followed by a rotation forest classifier, which we name FreshPRINCE, performs best. It is not state of the art, but it is significantly more accurate than nearest neighbour with dynamic time warping, and represents a reasonable benchmark for future comparison.