Abstract
Early time series classification (EarlyTSC) involves the prediction of a class label based on partial observation of a given time series. Most EarlyTSC algorithms consider the trade-off between accuracy and earliness as two competing objectives, using a single dedicated hyperparameter. To obtain insights into this trade-off requires finding a set of non-dominated (Pareto efficient) classifiers. So far, this has been approached through manual hyperparameter tuning. Since the trade-off hyperparameters only provide indirect control over the earliness-accuracy trade-off, manual tuning is tedious and tends to result in many sub-optimal hyperparameter settings. This complicates the search for optimal hyperparameter settings and forms a hurdle for the application of EarlyTSC to real-world problems. To address these issues, we propose an automated approach to hyperparameter tuning and algorithm selection for EarlyTSC, building on developments in the fast-moving research area known as automated machine learning (AutoML). To deal with the challenging task of optimising two conflicting objectives in early time series classification, we propose MultiETSC, a system for multi-objective algorithm selection and hyperparameter optimisation (MO-CASH) for EarlyTSC. MultiETSC can potentially leverage any existing or future EarlyTSC algorithm and produces a set of Pareto optimal algorithm configurations from which a user can choose a posteriori. As an additional benefit, our proposed framework can incorporate and leverage time-series classification algorithms not originally designed for EarlyTSC for improving performance on EarlyTSC; we demonstrate this property using a newly defined, “naïve” fixed-time algorithm. In an extensive empirical evaluation of our new approach on a benchmark of 115 data sets, we show that MultiETSC performs substantially better than baseline methods, ranking highest (avg. rank 1.98) compared to conceptually simpler single-algorithm (2.98) and single-objective alternatives (4.36).
Highlights
The goal of time series classification (TSC) is to assign a class label to a given time series, i.e., to a sequence of observations that have been sampled over time
According to the comparison of methods based on all metrics, MultiETSC performs significantly better than any of the algorithms we compared against, finding configurations that together dominate a larger portion of the objective space and distributed more evenly across the trade-off according to the -spread
MultiETSC performs significantly better than the single-objective combined algorithm selection and hyperparameter optimisation (CASH) method SO-All, which answers our second question
Summary
The goal of time series classification (TSC) is to assign a class label to a given time series, i.e., to a sequence of observations that have been sampled over time. Practical applications of time series classification include the diagnosis of heart conditions from ECGs, identification of patterns in financial markets, and detection of anomalies in seismic activity. Many such time-critical applications can benefit from classification results being available as early as possible, preferably even before the full time series has been observed. Cardiac surgical patients in postoperative care are monitored for postoperative complications during an extended period of time For some of these complications, indications of increased risk can be made far in advance of the actual onset (Abdelghani et al 2016). Being able to automatically detect these signals as soon as they occur, through a timely classification of the monitored time series, can mean the difference between life and death
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