TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification

Abstract

Time Series Classification (TSC) has seen enormous progress over the last two decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is the current state of the art in terms of classification accuracy. HIVE-COTE recognizes that time series data are a specific data type for which the traditional attribute-value representation, used predominantly in machine learning, fails to provide a relevant representation. HIVE-COTE combines multiple types of classifiers: each extracting information about a specific aspect of a time series, be it in the time domain, frequency domain or summarization of intervals within the series. However, HIVE-COTE (and its predecessor, FLAT-COTE) is often infeasible to run on even modest amounts of data. For instance, training HIVE-COTE on a dataset with only 1,500 time series can require 8 days of CPU time. It has polynomial runtime with respect to the training set size, so this problem compounds as data quantity increases. We propose a novel TSC algorithm, TS-CHIEF (Time Series Combination of Heterogeneous and Integrated Embedding Forest), which rivals HIVE-COTE in accuracy but requires only a fraction of the runtime. TS-CHIEF constructs an ensemble classifier that integrates the most effective embeddings of time series that research has developed in the last decade. It uses tree-structured classifiers to do so efficiently. We assess TS-CHIEF on 85 datasets of the University of California Riverside (UCR) archive, where it achieves state-of-the-art accuracy with scalability and efficiency. We demonstrate that TS-CHIEF can be trained on 130k time series in 2 days, a data quantity that is beyond the reach of any TSC algorithm with comparable accuracy.

arXiv comment37 pages, 10 figures

References in corpus (8)

• Deep learning for time series classification: a review
• Scalable and accurate deep learning for electronic health records
• Deep Learning for Sensor-based Activity Recognition: A Survey
• Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching
• Fast and Accurate Time Series Classification with WEASEL
• Proximity Forest: An effective and scalable distance-based classifier for time series
• Bag-of-Words Representation for Biomedical Time Series Classification
• Scalable Dictionary Classifiers for Time Series Classification

Cited by in corpus (14)

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• The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification
• Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms
• A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0
• The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier
• Minority Oversampling for Imbalanced Time Series Classification
• Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets
• dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification
• Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion
• Uncertain Time Series Classification With Shapelet Transform
• HIT-ROCKET: Hadamard-vector Inner-product Transformer for ROCKET