The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier

TL;DR

FreshPRINCE, a basic feature extractors using vector based classifiers shown to be effective with continuous attributes in current state-of-the-art time series classifiers, is tested on the UCR time series dataset archive.

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.

Figures (5)

• Figure 1: Visualisation of a simple pipeline algorithm for TSC. Could using standard transformers and vector based classifiers be as good as state of the art TSC algorithms?
• Figure 2: Relative rank performance of seven transforms used in a simple pipeline with a linear ridge classifier (a), XGBoost (b) and rotation forest (c). TSFresh and RandInt22 are significantly better than all other transforms with most base classifiers.
• Figure 3: Relative performance of three classifiers Rotation Forest, XBoost and RidgeCV (prefixes RotF, XG and Ridge) with two transforms TSFresh and RandIntCatch22 (suffix TSFr and RIC22). RotF is significantly better than the other classifiers, and RotF with TSFresh is the best overall combination.
• Figure 4: Pairwise scatter plots for TSFresh vs RandIntC22 with (a) RidgeCV, (b) XGBoost and (c) rotation forest, and (d) the scatter plot of using TSFresh with XGBoost with TSFresh. (a), (b) and (c) demonstrate the superiority of TSFresh over RandIntC22. (d) shows that rotation forest significantly outperforms XGBoost.
• Figure 5: Critical difference plot for FreshPRINCE against SOTA and DTW.