Detecting a multigranularity event in an unequal interval time series based on self-adaptive segmenting

2021 ◽  
Vol 25 (6) ◽  
pp. 1407-1429
Author(s):  
Haibo Li ◽  
Yongbo Yu
Keyword(s):  
Time Series ◽  
Event Detection ◽  
Time Series Data ◽  
Transport Systems ◽  
Series Data ◽  
Sampling Errors ◽  
Symbol Sequence ◽  
Interval Time ◽  
Interval Time Series ◽  
Self Adaptive

Analyzing the temporal behaviors and revealing the hidden rules of objects that produce time series data to detect the events that users are interested in have recently received a large amount of attention. Generally, in various application scenarios and most research works, the equal interval sampling of a time series is a requirement. However, this requirement is difficult to guarantee because of the presence of sampling errors in most situations. In this paper, a multigranularity event detection method for an unequal interval time series, called SSED (self-adaptive segmenting based event detection), is proposed. First, in view of the trend features of a time series, a self-adaptive segmenting algorithm is proposed to divide a time series into unfixed-length segmentations based on the trends. Then, by clustering the segmentations and mapping the clusters to different identical symbols, a symbol sequence is built. Finally, based on unfixed-length segmentations, the multigranularity events in the discrete symbol sequence are detected using a tree structure. The SSED is compared to two previous methods with ten public datasets. In addition, the SSED is applied to the public transport systems in Xiamen, China, using bus-speed time-series data. The experimental results show that the SSED can achieve higher efficiency and accuracy than existing algorithms.

scholarly journals Multiscale ordinal network analysis of human cardiac dynamics

2017 ◽  
Vol 375 (2096) ◽  
pp. 20160292 ◽  
Author(s):  
M. McCullough ◽  
M. Small ◽  
H. H. C. Iu ◽  
T. Stemler
Keyword(s):  
Time Series ◽  
Normal Sinus Rhythm ◽  
Series Data ◽  
Permutation Entropy ◽  
Multiscale Approach ◽  
Mapping Procedure ◽  
Interbeat Interval ◽  
Interval Time ◽  
Cardiac Dynamics ◽  
Interval Time Series

In this study, we propose a new information theoretic measure to quantify the complexity of biological systems based on time-series data. We demonstrate the potential of our method using two distinct applications to human cardiac dynamics. Firstly, we show that the method clearly discriminates between segments of electrocardiogram records characterized by normal sinus rhythm, ventricular tachycardia and ventricular fibrillation. Secondly, we investigate the multiscale complexity of cardiac dynamics with respect to age in healthy individuals using interbeat interval time series and compare our findings with a previous study which established a link between age and fractal-like long-range correlations. The method we use is an extension of the symbolic mapping procedure originally proposed for permutation entropy. We build a Markov chain of the dynamics based on order patterns in the time series which we call an ordinal network, and from this model compute an intuitive entropic measure of transitional complexity. A discussion of the model parameter space in terms of traditional time delay embedding provides a theoretical basis for our multiscale approach. As an ancillary discussion, we address the practical issue of node aliasing and how this effects ordinal network models of continuous systems from discrete time sampled data, such as interbeat interval time series. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.

scholarly journals CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA

2021 ◽  
Vol V-1-2021 ◽  
pp. 31-38
Author(s):  
M. Arnold ◽  
M. Hoyer ◽  
S. Keller
Keyword(s):  
Time Series ◽  
Event Detection ◽  
Time Series Data ◽  
Radar Data ◽  
Data Driven ◽  
Series Data ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Feature Based ◽  
Bridge Crossing

Abstract. This study focuses on detecting vehicle crossings (events) with ground-based interferometric radar (GBR) time series data recorded at bridges in the course of critical infrastructure monitoring. To address the challenging event detection and time series classification task, we rely on a deep learning (DL) architecture. The GBR-displacement data originates from real-world measurements at two German bridges under normal traffic conditions. As preprocessing, we only apply a low-pass filter. We develop and evaluate a one-dimensional convolutional neural network (CNN) to achieve a solely data-driven event detection. As a baseline machine learning approach, we use a Random Forest (RF) with a selected feature-based input. Both models’ performance is evaluated on two datasets by focusing on identifying events and pure bridge oscillations. Generally, the event classification results are promising, and the CNN outperforms the RF with an overall accuracy of 94.7% on the test subset. By relying on an entirely unknown second dataset, we focus on the models’ performances regarding the distinction between events and decays. On this dataset, the CNN meets this challenge successfully, while the feature-based RF classifies the majority of non-event decays as events. To sum up, the presented results reveal the potential of a data-driven DL approach concerning the detection of bridge crossing events in GBR-based displacement time series data. Based on such an event detection, a prospective assessment of bridge conditions seems feasible as an extension to previous structural health monitoring approaches.

A New Approach for Forecasting the Price Range With Financial Interval-Valued Time Series Data

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Wei Yang ◽  
Ai Han
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Statistical Tests ◽  
Series Data ◽  
Daily Data ◽  
Price Range ◽  
Out Of Sample ◽  
Interval Time Series

This paper proposes an interval-based methodology to model and forecast the price range or range-based volatility process of financial asset prices. Comparing with the existing volatility models, the proposed model utilizes more information contained in the interval time series than using the range information only or modeling the high and low price processes separately. An empirical study of the U.S. stock market daily data shows that the proposed interval-based model produces more accurate range forecasts than the classic point-based linear models for range process, in terms of both in-sample and out-of-sample forecasts. The statistical tests show that the forecasting advantages of the interval-based model are statistically significant in most cases. In addition, some stability tests have been conducted for ascertaining the advantages of the interval-based model through different sample windows and forecasting periods, which reveals similar results. This study provides a new interval-based perspective for volatility modeling and forecasting of financial time series data.

Acquisition of Embodied Knowledge on Gesture Motion by Singular Value Decomposition

2011 ◽  
Vol 15 (8) ◽  
pp. 1011-1018 ◽  
Author(s):  
Isao Hayashi ◽  
◽  
Yinlai Jiang ◽  
Shuoyu Wang ◽  
Keyword(s):  
Time Series ◽  
Singular Value Decomposition ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Singular Value ◽  
Series Data ◽  
Embodied Knowledge ◽  
Interval Time Series ◽  
Nonverbal Information ◽  
Value Decomposition

Communication is classified in terms of verbal and nonverbal information. We discuss an acquisition method of knowledge from nonverbal information. In particular, a gesture is an efficient form of nonverbal communication as well as in verbal ways, and we formulate here a method that measures similarity and estimation between gestures. A gesture includes human embodied knowledge, and therefore the visible bodily actions can communicate particular messages. However, we have infinite patterns for gesture, determined by personality. Recently, the singular spectrum analysis method is utilized as an attractive method. In this paper, we propose a new method for acquiring embodied knowledge from time-series data on gestures using singular value decomposition. The motion behavior is categorized into several clusters with similarity and estimation between interval time-series data. We discuss the usefulness of the proposed method using an example of gesture motion.

scholarly journals Subsurface primary production in the western subtropical North Pacific as evidence of large diapycnal diffusivity associated with the Subtropical Mode Water

2009 ◽  
Vol 6 (2) ◽  
pp. 1717-1734 ◽  
Author(s):  
C. Sukigara ◽  
T. Suga ◽  
T. Saino ◽  
K. Toyama ◽  
D. Yanagimoto ◽  
...  
Keyword(s):  
Time Series ◽  
Primary Production ◽  
Oxygen Sensor ◽  
Time Series Data ◽  
Early Summer ◽  
Series Data ◽  
Mode Water ◽  
Subtropical Mode Water ◽  
Vertical Diffusivity ◽  
Interval Time Series

Abstract. A profiling float equipped with a fluorometer, a dissolved oxygen sensor, and temperature and salinity sensors was deployed in the Subtropical Mode Water (STMW) formation region. It acquired quasi-Lagrangian, 5-day-interval time-series records from March to July 2006. The time-series distribution of chlorophyll a showed a sustained and sizable deep chlorophyll maximum at 50–80 m, just above the upper boundary of the STMW, throughout early summer (May–July). Vertically integrated chlorophyll values during this period consistently ranged from 15–30 mg m−2, indicating sustained primary production and a continuous supply of nutrients ranging from 10–30 mgN m−2 day−1. The time-series data showed no appreciable sporadic events of nutrient supply. Instead, our results support the recently measured large vertical diffusivity values (~5×10−4 m2 s

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