Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series

Hu, Bing; Rakthanmanon, Thanawin; Hao, Yuan; Evans, S.; Lonardi, Stefano; Keogh, Eamonn

doi:10.1007/s10618-014-0345-2

Cited by 20 publications

(15 citation statements)

References 46 publications

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“…The preprocessing of the data included the aggregation of both training and test subsets in one single dataset. In addition, a TS discretization was performed following the guidelines proposed by Lin et al [ 28 ] and Hu et al [ 29 ]; no feature selection was performed. None of the datasets contained missing values; therefore, no imputation was required.…”

Section: Resultsmentioning

confidence: 99%

Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data

Lemus

Beirão

Paunković

et al. 2019

Entropy

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Biomedical signals constitute time-series that sustain machine learning techniques to achieve classification. These signals are complex with measurements of several features over, eventually, an extended period. Characterizing whether the data can anticipate prediction is an essential task in time-series mining. The ability to obtain information in advance by having early knowledge about a specific event may be of great utility in many areas. Early classification arises as an extension of the time-series classification problem, given the need to obtain a reliable prediction as soon as possible. In this work, we propose an information-theoretic method, named Multivariate Correlations for Early Classification (MCEC), to characterize the early classification opportunity of a time-series. Experimental validation is performed on synthetic and benchmark data, confirming the ability of the MCEC algorithm to perform a trade-off between accuracy and earliness in a wide-spectrum of time-series data, such as those collected from sensors, images, spectrographs, and electrocardiograms.

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Section: Resultsmentioning

confidence: 99%

Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data

Lemus

Beirão

Paunković

et al. 2019

Entropy

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“…Single Plateau Class A Single Plateau Class B Center (NSIDC) in Boulder, Colorado and used as a critical resource for scientists studying climate change (Hu et al 2014).…”

Section: Factors Affecting the Best Warping Windowmentioning

confidence: 99%

Optimizing dynamic time warping’s window width for time series data mining applications

Dau

Silva

Petitjean

et al. 2018

Data Min Knowl Disc

Self Cite

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Dynamic Time Warping (DTW) is a highly competitive distance measure for most time series data mining problems. Obtaining the best performance from DTW requires setting its only parameter, the maximum amount of warping (w). In the supervised case with ample data, w is typically set by cross-validation in the training stage. However, this method is likely to yield suboptimal results for small training sets. For the unsupervised case, learning via cross-validation is not possible because we do not have access to labeled data. Many practitioners have thus resorted to assuming that "the larger the better", and they use the largest value of w permitted by the computational resources. However, as we will show, in most circumstances, this is a naïve approach that produces inferior clusterings. Moreover, the best warping window width is generally non-transferable between the two tasks, i.e., for a single dataset, practitioners cannot simply apply the best w learned for classification on clustering or vice versa. In addition, we will demonstrate that the appropriate amount of warping not only depends on the data structure, but also on the dataset size. Thus, even if a practitioner knows the best setting for a given dataset, they will likely be at a lost if they apply that setting on a bigger size version of that data. All these issues seem largely unknown or at least unappreciated in the community. In this work, we demonstrate the importance of setting DTW's warping window width correctly, and we also propose novel methods to learn this parameter in both supervised and unsupervised settings. The algorithms we propose to learn w can produce significant improvements in classification accuracy and clustering quality. We demonstrate the correctness of our novel observations and the utility of our ideas by testing them with more than one hundred publicly available datasets. Our forceful results allow us to make a perhaps unexpected claim; an underappreciated "low hanging fruit" in optimizing DTW's performance can produce improvements that make it an even stronger baseline, closing most or all the improvement gap of the more sophisticated methods proposed in recent years.

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“…To deal with the problem of predefined input length, many works have been proposed. For example, in [ 38 , 43 ], minimum description length or MDL is used to automatically discover intrinsic features and is utilized to detect anomalous ECGs. An adaptive window-based discord discovery (AWDD) [ 19 ] has been proposed to detect ECG anomaly.…”

Section: Related Workmentioning

confidence: 99%

“…To determine the length, it is, in fact, very difficult to know what the proper length is. Although some works [ 19 , 20 , 38 , 43 , 47 , 49 ] have presented their algorithms with variable lengths of results, and the length of the results may not be consistent with the length of actual cardiac cycle. Therefore, the result may not properly cover the cardiac cycle or morphology which is crucial for diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

Sivaraks

Ratanamahatana

2015

Computational and Mathematical Methods in Medicine

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Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

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Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series

Cited by 20 publications

References 46 publications

Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data

Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data

Optimizing dynamic time warping’s window width for time series data mining applications

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

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