TS-CHIEF: a scalable and accurate forest algorithm for time series classification

• Published in: Data mining and knowledge discovery Vol. 34; no. 3; pp. 742 - 775
• Main Authors: Shifaz, Ahmed, Pelletier, Charlotte, Petitjean, François, Webb, Geoffrey I.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2020; Springer Nature B.V; Springer
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Classification; Classifiers; Computer Science; Data Mining and Knowledge Discovery; Datasets; Information Storage and Retrieval; Machine learning; Physics; Polynomials; Representations; Run time (computers); Statistics for Engineering; Time series; Training; Bag of words; Metrics; Transformation; Scalable; Time series; Classification; Forest
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-020-00679-8

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 1500 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 130 k time series in 2 days, a data quantity that is beyond the reach of any TSC algorithm with comparable accuracy.