Time-Warping: A Time Series Data Augmentation of IMU Data for Construction Equipment Activity Identification

2019 ◽  
Author(s):  
Khandakar M. Rashid ◽  
Joseph Louis
Keyword(s):  
Time Series ◽  
Data Augmentation ◽  
Time Series Data ◽  
Construction Equipment ◽  
Series Data ◽  
Time Warping

scholarly journals Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London

2008 ◽  
Vol 5 (25) ◽  
pp. 885-897 ◽  
Author(s):  
Simon Cauchemez ◽  
Neil M Ferguson
Keyword(s):  
Time Series ◽  
Discrete Time ◽  
Continuous Time ◽  
Generation Time ◽  
Data Augmentation ◽  
Time Series Data ◽  
Observation Interval ◽  
Series Data ◽  
Time Model ◽  
Similar Time

We present a new statistical approach to analyse epidemic time-series data. A major difficulty for inference is that (i) the latent transmission process is partially observed and (ii) observed quantities are further aggregated temporally. We develop a data augmentation strategy to tackle these problems and introduce a diffusion process that mimicks the susceptible–infectious–removed (SIR) epidemic process, but that is more tractable analytically. While methods based on discrete-time models require epidemic and data collection processes to have similar time scales, our approach, based on a continuous-time model, is free of such constraint. Using simulated data, we found that all parameters of the SIR model, including the generation time, were estimated accurately if the observation interval was less than 2.5 times the generation time of the disease. Previous discrete-time TSIR models have been unable to estimate generation times, given that they assume the generation time is equal to the observation interval. However, we were unable to estimate the generation time of measles accurately from historical data. This indicates that simple models assuming homogenous mixing (even with age structure) of the type which are standard in mathematical epidemiology miss key features of epidemics in large populations.

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

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0197499 ◽  
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

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

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.

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

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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