Early classification of time series using multi-objective optimization techniques

Mori, Usue; Mendiburu, Alexander; Miranda, Isabel Marta; Lozano, José A.

doi:10.1016/j.ins.2019.04.024

Cited by 39 publications

(28 citation statements)

References 31 publications

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“…This introduces further complications, such as considering the sampling rate of individual time series, and balancing the time required to make predictions with the time it takes to wait for more data, and making the runtime of a classifier a critical issue. Recently, a multi-objective formulation of eTSC has been formulated (Mori et al 2019), deriving multiple solutions instead of picking a best one based on a single-objective function, combining earliness and accuracy. We will be looking into a multi-objective formulation of early classification for TEASER, too.…”

Section: Resultsmentioning

confidence: 99%

“…In (Mori et al 2019) eTSC is approached as a multi-objective optimization technique. As most solutions (such as TEASER) are based on combining the accuracy and earliness into a single-objective problem.…”

Section: Related Workmentioning

confidence: 99%

“…Their method dominates the state of the art in eTSC. For a meaningful comparison to TEASER, we would have to compare the multiple solutions of (Mori et al 2019) to our solution to see if one is better by computing the harmonic mean of the respective accuracy / earliness values. Unfortunately, neither the code nor the raw measurements of this method have been published.…”

Section: Related Workmentioning

confidence: 99%

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TEASER: early and accurate time series classification

Schäfer

Leser

2020

Data Min Knowl Disc

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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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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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TEASER: early and accurate time series classification

Schäfer

Leser

2020

Data Min Knowl Disc

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“…EarlyTSC introduces a second criterion to the classification problem: classifications do not only need to be accurate but also early. This results in a natural trade-off between accuracy and earliness (Mori et al 2019).…”

Section: Introductionmentioning

confidence: 99%

MultiETSC: automated machine learning for early time series classification

Ottervanger

Baratchi

Hoos

2021

Data Min Knowl Disc

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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).

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“…In order to improve the response time to anomalies, early classification techniques for time series [27] will be considered so that anomalous behavior can be detected before the operation has finished.…”

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confidence: 99%

Machine Tools Anomaly Detection Through Nearly Real-Time Data Analysis

Herranz

Antolínez

Escartin

et al. 2019

JMMP

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This work presents a new methodology for machine tools anomaly detection via operational data processing. The previous methodology has been field tested on a milling-boring machine in a real production environment. This paper also describes the data acquisition process, as well as the technical architecture needed for data processing. Subsequently, a technique for operational machine data segmentation based on dynamic time warping and hierarchical clustering is introduced. The formerly mentioned data segmentation and analysis technique allows for machine tools anomaly detection thanks to comparison between near real-time machine operational information, coming from strategically positioned sensors and outcomes collected from previous production cycles. Anomaly detection techniques shown in this article could achieve significant production improvements: “zero-defect manufacturing”, boosting factory efficiency, production plans scrap minimization, improvement of product quality, and the enhancement of overall equipment productivity.

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Early classification of time series using multi-objective optimization techniques

Cited by 39 publications

References 31 publications

TEASER: early and accurate time series classification

TEASER: early and accurate time series classification

MultiETSC: automated machine learning for early time series classification

Machine Tools Anomaly Detection Through Nearly Real-Time Data Analysis

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