Comparison of Trend Detection Algorithms in the Analysis of Physiological Time-Series Data

2005 ◽  
Vol 52 (4) ◽  
pp. 639-651 ◽  
Author(s):  
W.W. Melek ◽  
Z. Lu ◽  
A. Kapps ◽  
W.D. Fraser
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Trend Detection ◽  
Series Data ◽  
Detection Algorithms ◽  
Physiological Time

scholarly journals TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Time-Series Data in Circadian Biology Research

2020 ◽  
Vol 35 (5) ◽  
pp. 439-451 ◽  
Author(s):  
Elan Ness-Cohn ◽  
Marta Iwanaszko ◽  
William L. Kath ◽  
Ravi Allada ◽  
Rosemary Braun
Keyword(s):  
Time Series ◽  
Experimental Design ◽  
Time Series Data ◽  
Synthetic Data ◽  
Optimal Choice ◽  
Series Data ◽  
Detection Accuracy ◽  
Periodic Patterns ◽  
Detection Algorithms ◽  
The Impact

The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues, coordinating physiological processes. The effect of this rhythm on health has generated increasing interest in discovering genes under circadian control by searching for periodic patterns in transcriptomic time-series experiments. While algorithms for detecting cycling transcripts have advanced, there remains little guidance quantifying the effect of experimental design and analysis choices on cycling detection accuracy. We present TimeTrial, a user-friendly benchmarking framework using both real and synthetic data to investigate cycle detection algorithms’ performance and improve circadian experimental design. Results show that the optimal choice of analysis method depends on the sampling scheme, noise level, and shape of the waveform of interest and provides guidance on the impact of sampling frequency and duration on cycling detection accuracy. The TimeTrial software is freely available for download and may also be accessed through a web interface. By supplying a tool to vary and optimize experimental design considerations, TimeTrial will enhance circadian transcriptomics studies.

scholarly journals Predicting delayed cerebral ischemia after subarachnoid hemorrhage using physiological time series data

2018 ◽  
Vol 33 (1) ◽  
pp. 95-105 ◽  
Author(s):  
Soojin Park ◽  
Murad Megjhani ◽  
Hans-Peter Frey ◽  
Edouard Grave ◽  
Chris Wiggins ◽  
...  
Keyword(s):  
Time Series ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Ischemia ◽  
Time Series Data ◽  
Delayed Cerebral Ischemia ◽  
Series Data ◽  
Physiological Time

scholarly journals Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images

2019 ◽  
Vol 8 (1) ◽  
pp. 36 ◽  
Author(s):  
Bingxin Bai ◽  
Yumin Tan ◽  
Dong Guo ◽  
Bo Xu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Time Series Data ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Dynamic Monitoring ◽  
Trend Detection ◽  
Series Data ◽  
Forest Land ◽  
Remote Sensing Images

Time series remote sensing images can be used to monitor the dynamic changes of forest lands. Due to consistent cloud cover and fog, a single sensor typically provides limited data for dynamic monitoring. This problem is solved by combining observations from multiple sensors to form a time series (a satellite image time series). In this paper, the pixel-based multi-source remote sensing image fusion (MulTiFuse) method is applied to combine the Landsat time series and Huanjing-1 A/B (HJ-1 A/B) data in the Fuling district of Chongqing, China. The fusion results are further corrected and improved with spatial features. Dynamic monitoring and analysis of the study area are subsequently performed on the improved time series data using the combination of Mann-Kendall trend detection method and Theil Sen Slope analysis. The monitoring results show that a majority of the forest land (60.08%) has experienced strong growth during the 1999–2013 period. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracies.

scholarly journals Evaluation metrics for anomaly detection algorithms in time-series

2019 ◽  
Vol 11 (2) ◽  
pp. 113-130 ◽  
Author(s):  
György Kovács ◽  
Gheorghe Sebestyen ◽  
Anca Hangan
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Confusion Matrix ◽  
Series Data ◽  
Time Data ◽  
Temporal Dimension ◽  
Detection Techniques ◽  
Detection Algorithms

Abstract Time-series are ordered sequences of discrete-time data. Due to their temporal dimension, anomaly detection techniques used in time-series have to take into consideration time correlations and other time-related particularities. Generally, in order to evaluate the quality of an anomaly detection technique, the confusion matrix and its derived metrics such as precision and recall are used. These metrics, however, do not take this temporal dimension into consideration. In this paper, we propose three metrics that can be used to evaluate the quality of a classification, while accounting for the temporal dimension found in time-series data.

scholarly journals Tsunami arrival time detection system applicable to discontinuous time series data with outliers

2016 ◽  
Vol 16 (12) ◽  
pp. 2603-2622
Author(s):  
Jun-Whan Lee ◽  
Sun-Cheon Park ◽  
Duk Kee Lee ◽  
Jong Ho Lee
Keyword(s):  
Time Series ◽  
Arrival Time ◽  
Time Series Data ◽  
Detection System ◽  
Detection Algorithm ◽  
Series Data ◽  
False Alarms ◽  
Detection Algorithms ◽  
Tsunami Detection ◽  
Tsunami Signal

Abstract. Timely detection of tsunamis with water level records is a critical but logistically challenging task because of outliers and gaps. Since tsunami detection algorithms require several hours of past data, outliers could cause false alarms, and gaps can stop the tsunami detection algorithm even after the recording is restarted. In order to avoid such false alarms and time delays, we propose the Tsunami Arrival time Detection System (TADS), which can be applied to discontinuous time series data with outliers. TADS consists of three algorithms, outlier removal, gap filling, and tsunami detection, which are designed to update whenever new data are acquired. After calibrating the thresholds and parameters for the Ulleung-do surge gauge located in the East Sea (Sea of Japan), Korea, the performance of TADS was discussed based on a 1-year dataset with historical tsunamis and synthetic tsunamis. The results show that the overall performance of TADS is effective in detecting a tsunami signal superimposed on both outliers and gaps.

Recognizing diseases from physiological time series data

2018 ◽  
Vol 16 (3) ◽  
pp. 167-172
Author(s):  
Danni Wang ◽  
Lin Zhou ◽  
Li Liu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Physiological Time
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