A fast and accurate interval-based classifier for time series classification. This repository contains the source code for the STSF algorithm, published in the paper:
Nestor Cabello, Elham Naghizade, Jianzhong Qi, and Lars Kulik. Fast and Accurate Time Series Classification Through Supervised Interval Search. In Proceedings of the 20th International Conference on Data Mining (ICDM 2020), (acceptance rate: 9.8%), November 17-20, 2020, Virtual Event, Sorrento, Italy, 6 pages.
Time series classification (TSC) aims to predict the class label of a given time series. Modern applications such as appliance modelling require to model an abundance of long time series, which makes it difficult to use many state-of-the-art TSC techniques due to their high computational cost and lack of interpretable outputs. To address these challenges, we propose a novel TSC method: the Supervised Time Series Forest (STSF). STSF improves the classification efficiency by examining only a (set of) sub-series of the original time series, and its tree-based structure allows for interpretable outcomes. STSF adapts a top-down approach to search for relevant sub- series in three different time series representations prior to training any tree classifier, where the relevance of a sub-series is measured by feature ranking metrics (i.e., supervision signals). Experiments on extensive real datasets show that STSF achieves comparable accuracy to state-of-the-art TSC methods while being significantly more efficient, enabling TSC for long time series.
For a working example run --> STSF code/Main.m
A significantly more accurate and extremely fast interval-based classifier based on ideas from STSF can be found in:
Cabello, N., Naghizade, E., Qi, J. et al. Fast, accurate and explainable time series classification through randomization. Data Min Knowl Disc (2023). https://doi.org/10.1007/s10618-023-00978-w