Two-Phase Imputation with Regional-Gradient-Guided Bootstrapping Algorithm and Dynamics Time Warping for Incomplete Time Series Data

Intermittent gas and liquid two-phase flow was generated in a 6 m × 67 mm diameter pipe mounted rotatable frame (vertical up to −20°). Air and a 5 mPa s silicone oil at atmospheric pressure were studied. Gas superficial velocities between 0.17 and 2.9 m/s and liquid superficial velocities between 0.023 and 0.47 m/s were employed. These runs were repeated at 7 angles making a total of 420 runs. Cross sectional void fraction time series were measured over 60 seconds for each run using a Wire Mesh Sensor and a twin plane Electrical Capacitance Tomography. The void fraction time series data were analysed in order to extract average void fraction, structure velocities and structure frequencies. Results are presented to illustrate the effect of the angle as well as the phase superficial velocities affect the intermittent flows behaviour. Existing correlations suggested to predict average void fraction and gas structures velocity and frequency in slug flow have been compared with new experimental results for any intermittent flow including: slug, cap bubble and churn. Good agreements have been seen for the gas structure velocity and mean void fraction. On the other hand, no correlation was found to predict the gas structure frequency, especially in vertical and inclined pipes.

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An OGS-Based Dynamic Time Warping Algorithm for Time Series Data

Contributions to Economics - Innovation in the High-Tech Economy ◽

10.1007/978-3-642-41585-2_10 ◽

2013 ◽

pp. 115-121

Author(s):

Mi Zhou

Keyword(s):

Time Series ◽

Dynamic Time Warping ◽

Time Series Data ◽

Series Data ◽

Time Warping ◽

Dynamic Time

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Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

PLoS ONE ◽

10.1371/journal.pone.0197499 ◽

2018 ◽

Vol 13 (5) ◽

pp. e0197499 ◽

Cited By ~ 2

Author(s):

Yongli Liu ◽

Jingli Chen ◽

Shuai Wu ◽

Zhizhong Liu ◽

Hao Chao

Keyword(s):

Time Series ◽

Dynamic Time Warping ◽

Time Series Data ◽

Series Data ◽

Time Warping ◽

Dynamic Time

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A SAS-IML program for implementing two-phase regression analysis of geophysical time series data

Computers & Geosciences ◽

10.1016/s0098-3004(97)00049-6 ◽

1997 ◽

Vol 23 (7) ◽

pp. 763-770 ◽

Cited By ~ 6

Author(s):

Gerri M. Dunnigan ◽

John L. Hammen ◽

T.Robert Harris

Keyword(s):

Time Series ◽

Regression Analysis ◽

Time Series Data ◽

Series Data ◽

Two Phase ◽

Geophysical Time Series

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Free congruence: an exploration of expanded similarity measures for time series data

10.21203/rs.3.rs-163245/v1 ◽

2021 ◽

Author(s):

Lucas Cassiel Jacaruso

Keyword(s):

Time Series ◽

Dynamic Time Warping ◽

Time Series Data ◽

Similarity Measures ◽

Distance Measures ◽

Series Data ◽

Time Warping ◽

Point To Point ◽

Dynamic Time ◽

Point Distance

Abstract Time series similarity measures are highly relevant in a wide range of emerging applications including training machine learning models, classification, and predictive modeling. Standard similarity measures for time series most often involve point-to-point distance measures including Euclidean distance and Dynamic Time Warping. Such similarity measures fundamentally require the fluctuation of values in the time series being compared to follow a corresponding order or cadence for similarity to be established. Other existing approaches use local statistical tests to detect structural changes in time series. This paper is spurred by the exploration of a broader definition of similarity, namely one that takes into account the sheer numerical resemblance between sets of statistical properties for time series segments irrespectively of value labeling. Further, the presence of common pattern components between time series segments was examined even if they occur in a permuted order, which would not necessarily satisfy the criteria of more conventional point-to-point distance measures. The newly defined similarity measures were tested on time series data representing over 20 years of cooperation intent expressed in global media sentiment. Tests determined whether the newly defined similarity measures would accurately identify stronger resemblance, on average, for pairings of similar time series segments (exhibiting overall decline) than pairings of differing segments (exhibiting overall decline and overall rise). The ability to identify patterns other than the obvious overall rise or decline that can accurately relate samples is regarded as a first step towards assessing the value of the newly explored similarity measures for classification or prediction. Results were compared with those of Dynamic Time Warping on the same data for context. Surprisingly, the test for numerical resemblance between sets of statistical properties established stronger resemblance for pairings of decline years with greater statistical significance than Dynamic Time Warping on the particular data and sample size used.

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Exploring the Spatial-Temporal Characteristics of Traditional Public Bicycle Use in Yancheng, China: A Perspective of Time Series Cluster of Stations

Sustainability ◽

10.3390/su12166370 ◽

2020 ◽

Vol 12 (16) ◽

pp. 6370

Author(s):

Zhan Gao ◽

Sheng Wei ◽

Lei Wang ◽

Sijia Fan

Keyword(s):

Time Series ◽

Time Series Data ◽

Urban Environments ◽

Series Data ◽

Sequential Data ◽

Time Warping ◽

Large Cities ◽

Points Of Interest ◽

Transportation Development ◽

Dynamic Time

Traditional dock-based public bicycle systems continue to dominate cycling in most cities, even though bicycle-sharing services are an increasingly popular means of transportation in many of China’s large cities. A few studies investigated the traditional public bicycle systems in small and mid-sized cities in China. The time series clustering method’s advantages for analyzing sequential data used in many transportation-related studies are restricted to time series data, thereby limiting applications to transportation planning. This study explores the characteristics of a typical third-tier city’s public bicycle system (where there is no bicycle-sharing service) using station classification via the time series cluster algorithm and bicycle use data. A dynamic time warping distance-based k-medoids method classifies public bicycle stations by using one-month bicycle use data. The method is further extended to non-time series data after format conversion. The paper identified three clusters of stations and analyzed the relationships between clusters’ features and the stations’ urban environments. Based on points-of-interest data, the classification results were validated using the enrichment factor and the proportional factor. The method developed in this paper can apply to other transportation analysis and the results also yielded relevant strategies for transportation development and planning.

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