scholarly journals A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems

2017 ◽  
Author(s):  
Yingchi Mao ◽  
Haishi Zhong ◽  
Xianjian Xiao ◽  
Xiaofang Li
Keyword(s):  
Similarity Measure ◽  
Dynamic Time Warping ◽  
Sampling Methods ◽  
Transportation Systems ◽  
Smart Devices ◽  
Trajectory Data ◽  
Time Warping ◽  
Noise Data ◽  
Trajectory Similarity ◽  
Dynamic Time

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

scholarly journals Satellite Image Time Series Clustering via Time Adaptive Optimal Transport

2021 ◽  
Vol 13 (19) ◽  
pp. 3993
Author(s):  
Zheng Zhang ◽  
Ping Tang ◽  
Weixiong Zhang ◽  
Liang Tang
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Dynamic Time Warping ◽  
Optimal Transport ◽  
Satellite Image ◽  
Time Warping ◽  
Unsupervised Analysis ◽  
Training Samples ◽  
Dynamic Time ◽  
Visual Results

Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.

scholarly journals A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis

2020 ◽  
Author(s):  
Lu Bai ◽  
Lixin Cui ◽  
Yue Wang ◽  
Yuhang Jiao ◽  
Edwin R. Hancock
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Similarity Measure ◽  
Dynamic Time Warping ◽  
Financial Time Series ◽  
Time Varying ◽  
Time Warping ◽  
Classical Dynamic ◽  
Financial Time ◽  
Dynamic Time

Network representations are powerful tools for the analysis of time-varying financial complex systems consisting of multiple co-evolving financial time series, e.g., stock prices, etc. In this work, we develop a new kernel-based similarity measure between dynamic time-varying financial networks. Our ideas is to transform each original financial network into quantum-based entropy time series and compute the similarity measure based on the classical dynamic time warping framework associated with the entropy time series. The proposed method bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks abstracted from financial time series of New York Stock Exchange (NYSE) database demonstrate that our approach can effectively discriminate the abrupt structural changes in terms of the extreme financial events.

scholarly journals Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System

2019 ◽  
Vol 8 (6) ◽  
pp. 281
Author(s):  
Xiaofei Zhao ◽  
Caiyi Hu ◽  
Zhao Liu ◽  
Yangyang Meng
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Dynamic Time Warping ◽  
Travel Demand ◽  
Time Series Data ◽  
Transportation Systems ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Time ◽  
Grid Based

Many kinds of spatial–temporal data collected by transportation systems, such as user order systems or automated fare-collection (AFC) systems, can be discretized and converted into time-series data. With the technique of time-series data mining, certain travel-demand patterns of different areas in the city can be detected. This study proposes a data-mining model for understanding the patterns and regularities of human activities in urban areas from spatiotemporal datasets. This model uses a grid-based method to convert spatiotemporal point datasets into discretized temporal sequences. Time-series analysis technique dynamic time warping (DTW) is then used to describe the similarity between travel-demand sequences, while the clustering algorithm density-based spatial clustering of applications with noise (DBSCAN), based on modified DTW, is used to detect clusters among the travel-demand samples. Four typical patterns are found, including balanced and unbalanced cases. These findings can help to understand the land-use structure and commuting activities of a city. The results indicate that the grid-based model and time-series analysis model developed in this study can effectively uncover the spatiotemporal characteristics of travel demand from usage data in public transportation systems.

scholarly journals Fast Constrained Dynamic Time Warping for Similarity Measure of Time Series Data

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 222841-222858
Author(s):  
Wonyoung Choi ◽  
Jaechan Cho ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Time

scholarly journals Using time-series similarity measures to compare animal movement trajectories in ecology

2019 ◽  
Vol 73 (11) ◽  
Author(s):  
Ian R. Cleasby ◽  
Ewan D. Wakefield ◽  
Barbara J. Morrissey ◽  
Thomas W. Bodey ◽  
Steven C. Votier ◽  
...  
Keyword(s):  
Dynamic Time Warping ◽  
Similarity Measures ◽  
Simulated Data ◽  
Time Warping ◽  
Fréchet Distance ◽  
Movement Trajectories ◽  
Movement Data ◽  
Frechet Distance ◽  
Trajectory Similarity ◽  
Dynamic Time

Abstract Identifying and understanding patterns in movement data are amongst the principal aims of movement ecology. By quantifying the similarity of movement trajectories, inferences can be made about diverse processes, ranging from individual specialisation to the ontogeny of foraging strategies. Movement analysis is not unique to ecology however, and methods for estimating the similarity of movement trajectories have been developed in other fields but are currently under-utilised by ecologists. Here, we introduce five commonly used measures of trajectory similarity: dynamic time warping (DTW), longest common subsequence (LCSS), edit distance for real sequences (EDR), Fréchet distance and nearest neighbour distance (NND), of which only NND is routinely used by ecologists. We investigate the performance of each of these measures by simulating movement trajectories using an Ornstein-Uhlenbeck (OU) model in which we varied the following parameters: (1) the point of attraction, (2) the strength of attraction to this point and (3) the noise or volatility added to the movement process in order to determine which measures were most responsive to such changes. In addition, we demonstrate how these measures can be applied using movement trajectories of breeding northern gannets (Morus bassanus) by performing trajectory clustering on a large ecological dataset. Simulations showed that DTW and Fréchet distance were most responsive to changes in movement parameters and were able to distinguish between all the different parameter combinations we trialled. In contrast, NND was the least sensitive measure trialled. When applied to our gannet dataset, the five similarity measures were highly correlated despite differences in their underlying calculation. Clustering of trajectories within and across individuals allowed us to easily visualise and compare patterns of space use over time across a large dataset. Trajectory clusters reflected the bearing on which birds departed the colony and highlighted the use of well-known bathymetric features. As both the volume of movement data and the need to quantify similarity amongst animal trajectories grow, the measures described here and the bridge they provide to other fields of research will become increasingly useful in ecology. Significance statement As the use of tracking technology increases, there is a need to develop analytical techniques to process such large volumes of data. One area in which this would be useful is the comparison of individual movement trajectories. In response, a variety of measures of trajectory similarity have been developed within the information sciences. However, such measures are rarely used by ecologists who may be unaware of them. To remedy this, we apply five common measures of trajectory similarity to both simulated data and real ecological dataset comprising of movement trajectories of breeding northern gannets. Dynamic time warping and Fréchet distance performed best on simulated data. Using trajectory similarity measures on our gannet dataset, we identified distinct foraging clusters centred on different bathymetric features, demonstrating one application of such similarity measures. As new technology and analysis techniques proliferate across ecology and the information sciences, closer ties between these fields promise further innovative analysis of movement data.

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