Dynamic Time Warping: Itakura vs Sakoe-Chiba

Geler, Zoltan; Kurbalija, Vladimir; Ivanović, Mirjana; Radovanović, Miloš; Dai, Weihui

doi:10.1109/inista.2019.8778300

Cited by 29 publications

(20 citation statements)

References 31 publications

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“…A constraining range is set around the selected data point to ensure that only data points near the selected point will be compared while data points that are far away will not be considered. We used the Sakoe–Chiba method to create our constraining range, but other constraining methods exist (e.g., Itakura parallelogram) . Next, the signal intensities of the reference data points residing inside the constraining range are subtracted from the intensity of y s,8 (Figure E).…”

Section: Resultsmentioning

confidence: 99%

“…“Continuity” and “monotonicity” constraints are typically imposed in DTW algorithms to ensure that features are not ignored or repeated, respectively . Additionally, commonly used boundary conditions, such as the Sakoe–Chiba or Itakura parallelogram methods, place constraints on the time window during warping to prevent matching of features that are far apart . The combination of constraints provides more accurate and rapid analysis than without restrictions and also reduces the chances of obtaining a singularity, albeit at the expense of “optimal” alignment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Time-Warping Correction for Shifts in Ultrahigh Resolving Power Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations

Hollerbach

Conant

Nagy

et al. 2021

J. Am. Soc. Mass Spectrom.

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Detection of arrival time shifts between ion mobility spectrometry (IMS) separations can limit achievable resolving power (Rp), particularly when multiple separations are summed or averaged, as commonly practiced in IMS. Such variations can be apparent in higher Rp measurements and are particularly evident in long path length traveling wave structures for lossless ion manipulations (SLIM) IMS due to their typically much longer separation times. Here, we explore data processing approaches employing single value alignment (SVA) and nonlinear dynamic time warping (DTW) to correct for variations between IMS separations, such as due to pressure fluctuations, to enable more effective spectrum summation for improving Rp and detection of low-intensity species. For multipass SLIM IMS separations, where narrow mobility range measurements have arrival times that can extend to several seconds, the SVA approach effectively corrected for such variations and significantly improved Rp for summed separations. However, SVA was much less effective for broad mobility range separations, such as obtained with multilevel SLIM IMS. Changes in ions’ arrival times were observed to be correlated with small pressure changes, with approximately 0.6% relative arrival time shifts being common, sufficient to result in a loss of Rp for summed separations. Comparison of the approaches showed that DTW alignment performed similarly to SVA when used over a narrow mobility range but was significantly better (providing narrower peaks and higher signal intensities) for wide mobility range data. We found that the DTW approach increased Rp by as much as 115% for measurements in which 50 IMS separations over 2 s were summed. We conclude that DTW is superior to SVA for ultra-high-resolution broad mobility range SLIM IMS separations and leads to a large improvement in effective Rp, correcting for ion arrival time shifts regardless of the cause, as well as improving the detectability of low-abundance species. Our tool is publicly available for use with universal ion mobility format (.UIMF) and text (.txt) files.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Time-Warping Correction for Shifts in Ultrahigh Resolving Power Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations

Hollerbach

Conant

Nagy

et al. 2021

J. Am. Soc. Mass Spectrom.

View full text Add to dashboard Cite

show abstract

“…Que proposed a data-driven integrated framework for health prognostics for steam turbines, which is based on extreme gradient boosting (XGBoost) and dynamic time warping (DTW). And the proposed framework has achieved good results in practical application [23]. A novel technique for the exact indexing of DTW is proposed by E. Keogh [24] and the accuracy and effectiveness of the method have been proved.…”

Section: Introductionmentioning

confidence: 89%

Time Series Risk Prediction Based on LSTM and a Variant DTW Algorithm: Application of Bed Inventory Overturn Prevention in a Pant-Leg CFB Boiler

et al. 2020

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The pant-leg design is typical for higher capacity circulating fluidized bed (CFB) boilers because it allows for better secondary air penetration, maintaining good air-coal mixing and efficient combustion. However, the special risk, nominated as bed inventory overturn, remains a big challenge and it hinders the application of pant-leg CFB boilers. For a time series risk, it is critical to do the bed inventory overturn prevention to leave enough time for the adjustment. This paper proposed a new framework combing long short-term memory (LSTM) and dynamic time warping (DTW) methods to do the risk prediction. Pattern matching of data difference discrimination is employed for DTW algorithm, instead of the traditional Euclidean metric. The pattern matching has the merits in reduction of calculation and improvement of the adaptability to variables with different dimensions. After variable processing of the time series data by the variant DTW algorithm, the bed pressure drop prediction model is established based on the LSTM structure in this framework. Compared with some traditional prediction method, the framework in this paper has achieved superior results in the application of bed inventory overturn prevention.

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“…For a given province and a given forecast origin, three sets containing the most similar provinces concerning the different parameters

, and

were retrieved. The distance between the two series was computed using dynamic time warping (DTW) with Itakura constraint to allow for some small temporal shifts between the series [ 32 ]. The similarity was considered only in the last 30 days before the origin, assuming that only the most recent past was of interest.…”

Section: Methodsmentioning

confidence: 99%

Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

Ferrari

Gerardi

Manzi

et al. 2021

IJERPH

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In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

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Dynamic Time Warping: Itakura vs Sakoe-Chiba

Cited by 29 publications

References 31 publications

Dynamic Time-Warping Correction for Shifts in Ultrahigh Resolving Power Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations

Dynamic Time-Warping Correction for Shifts in Ultrahigh Resolving Power Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations

Time Series Risk Prediction Based on LSTM and a Variant DTW Algorithm: Application of Bed Inventory Overturn Prevention in a Pant-Leg CFB Boiler

Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

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