Classifiers with a reject option for early time-series classification

Diagnosis of bearing faults is crucial in various industries. Time series classification (TSC) assigns each time series to one of a set of pre-defined classes, such as normal and fault, and has been regarded as an appropriate approach for bearing fault diagnosis. Considering late and inaccurate fault diagnosis may have a significant impact on maintenance costs, it is important to classify bearing signals as early and accurately as possible. TSC, however, has a major limitation, which is that a time series cannot be classified until the entire series is collected, implying that a fault cannot be diagnosed using TSC in advance. Therefore, it is important to classify a partially collected time series for early time series classification (ESTC), which is a TSC that considers both accuracy and earliness. Feature-based TSCs can handle this, but the problem is to determine whether a partially collected time series is enough for a decision that is still unsolved. Motivated by this, we propose an indicator of data sufficiency to determine whether a feature-based fault detection classifier can start classifying partially collected signals in order to diagnose bearing faults as early and accurately as possible. The indicator is trained based on the cosine similarity between signals that were collected fully and partially as input to the classifier. In addition, a parameter setting method for efficiently training the indicator is also proposed. The results of experiments using four benchmark datasets verified that the proposed indicator increased both accuracy and earliness compared with the previous time series classification method and general time series classification.

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DTEC: Distance Transformation Based Early Time Series Classification

Proceedings of the 2019 SIAM International Conference on Data Mining ◽

10.1137/1.9781611975673.55 ◽

2019 ◽

pp. 486-494 ◽

Cited By ~ 4

Author(s):

Liuyi Yao ◽

Yaliang Li ◽

Yezheng Li ◽

Hengtong Zhang ◽

Mengdi Huai ◽

...

Keyword(s):

Time Series ◽

Early Time ◽

Time Series Classification ◽

Distance Transformation

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TEASER: early and accurate time series classification

Data Mining and Knowledge Discovery ◽

10.1007/s10618-020-00690-z ◽

2020 ◽

Vol 34 (5) ◽

pp. 1336-1362

Author(s):

Patrick Schäfer ◽

Ulf Leser

Keyword(s):

Time Series ◽

Real Life ◽

Early Time ◽

Classification Problem ◽

Critical Issue ◽

Decision Time ◽

Superior Performance ◽

Optimal Decision ◽

Time Series Classification ◽

Start Time

Abstract Early time series classification (eTSC) is the problem of classifying a time series after as few measurements as possible with the highest possible accuracy. The most critical issue of any eTSC method is to decide when enough data of a time series has been seen to take a decision: Waiting for more data points usually makes the classification problem easier but delays the time in which a classification is made; in contrast, earlier classification has to cope with less input data, often leading to inferior accuracy. The state-of-the-art eTSC methods compute a fixed optimal decision time assuming that every times series has the same defined start time (like turning on a machine). However, in many real-life applications measurements start at arbitrary times (like measuring heartbeats of a patient), implying that the best time for taking a decision varies widely between time series. We present TEASER, a novel algorithm that models eTSC as a two-tier classification problem: In the first tier, a classifier periodically assesses the incoming time series to compute class probabilities. However, these class probabilities are only used as output label if a second-tier classifier decides that the predicted label is reliable enough, which can happen after a different number of measurements. In an evaluation using 45 benchmark datasets, TEASER is two to three times earlier at predictions than its competitors while reaching the same or an even higher classification accuracy. We further show TEASER’s superior performance using real-life use cases, namely energy monitoring, and gait detection.

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Early time-series classification with reliability guarantee.

10.2172/1051704 ◽

2012 ◽

Author(s):

Maya R. Gupta ◽

Nathan Parrish ◽

Hyrum S. Anderson

Keyword(s):

Time Series ◽

Early Time ◽

Time Series Classification

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MultiETSC: automated machine learning for early time series classification

Data Mining and Knowledge Discovery ◽

10.1007/s10618-021-00781-5 ◽

2021 ◽

Author(s):

Gilles Ottervanger ◽

Mitra Baratchi ◽

Holger H. Hoos

Keyword(s):

Machine Learning ◽

Time Series ◽

Optimal Algorithm ◽

Empirical Evaluation ◽

Early Time ◽

Time Series Classification ◽

Algorithm Selection ◽

Trade Off ◽

Conflicting Objectives ◽

Automated Machine Learning

AbstractEarly 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).

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