Dynamic Spatiotemporal Causality Analysis for Network Traffic Flow Based on Transfer Entropy and Sliding Window Approach

With the rapid development of sensor and communication technologies, a large amount of spatiotemporal traffic data has been accumulated, presenting the characteristics of big data. The potential information and regularity of traffic state evolution can be extracted from the huge traffic flow time series data and applied to intelligent transportation systems. This study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network. Transfer entropy algorithm is utilized to detect the spatiotemporal causality of network traffic states based on the extensive traffic time series data, which could measure the amount and direction of information transmission. A combination of Gaussian kernel density estimation and sliding window approach is proposed to calculate the transfer entropy and construct dynamic spatiotemporal causality graphs based on the causality significance test. The indexes of affected coefficient, influence coefficient, input degree, and output degree are defined to evaluate the causal interaction of traffic states among different road segments and identify the critical roads and potential bottlenecks of the existing road network. Experimental results based on real-world traffic sensor data indicate that the structures of traffic causality graphs are time-varying; the traffic cause-effect interaction among different road segments during the peak time is more significant than that during the nonpeak time; and the critical road segments can be identified, which are mainly located at the intersections of arterial roads, undertaking the convergence and dispersion of large traffic flows.

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Development of A Pharmaceutical Early Warning Scoring Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

10.2196/preprints.21720 ◽

2020 ◽

Author(s):

Hsiao-Ko Chang ◽

Hui-Chih Wang ◽

Chih-Fen Huang ◽

Feipei Lai

Keyword(s):

Time Series ◽

Cardiac Arrest ◽

Early Warning ◽

Time Series Data ◽

Predictive Accuracy ◽

Vital Signs ◽

Sliding Window ◽

Early Warning Score ◽

Series Data ◽

Dynamic Time

BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a pharmaceutical early warning model to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose a new early warning score model for detecting cardiac arrest via pharmaceutical classification and by using a sliding window; we apply learning-based algorithms to time-series data for a Pharmaceutical Early Warning Scoring Model (PEWSM). By treating pharmaceutical features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits, and replenishers and regulators of water and electrolytes. The best AUROC of bits is 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, LSTM yields better performance with time-series data. The proposed PEWSM, which offers 4-hour predictions, is better than the National Early Warning Score (NEWS) in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

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Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

Security and Communication Networks ◽

10.1155/2021/6665984 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Jing Zhao ◽

Shubo Liu ◽

Xingxing Xiong ◽

Zhaohui Cai

Keyword(s):

Time Series ◽

Privacy Protection ◽

Large Scale ◽

Differential Privacy ◽

Time Series Data ◽

Sliding Window ◽

Data Publishing ◽

Series Data ◽

Data Publication ◽

Autocorrelation Time

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

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Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.33.13890 ◽

2018 ◽

Vol 7 (3.3) ◽

pp. 218 ◽

Cited By ~ 2

Author(s):

D Senthil ◽

G Suseendran

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Association Rule ◽

Association Rule Mining ◽

Time Series Data ◽

Sliding Window ◽

Series Data ◽

Support Vector ◽

Rule Mining ◽

Window Technique

Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.

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TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy

BMC Neuroscience ◽

10.1186/1471-2202-12-119 ◽

2011 ◽

Vol 12 (1) ◽

pp. 119 ◽

Cited By ~ 120

Author(s):

Michael Lindner ◽

Raul Vicente ◽

Viola Priesemann ◽

Michael Wibral

Keyword(s):

Time Series ◽

Open Source ◽

Information Flow ◽

Time Series Data ◽

Transfer Entropy ◽

Series Data

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Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling

Geofluids ◽

10.1155/2020/8851475 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Yi-Hui Pang ◽

Hong-Bo Wang ◽

Jian-Jian Zhao ◽

De-Yong Shang

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Arima Model ◽

Sliding Window ◽

Series Data ◽

Average Model ◽

Hydraulic Support ◽

Autoregressive Integrated Moving Average ◽

Moving Average Model

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

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Structural network inference from time-series data using a generative model and transfer entropy

Pattern Recognition Letters ◽

10.1016/j.patrec.2019.05.019 ◽

2019 ◽

Vol 125 ◽

pp. 357-363 ◽

Cited By ~ 2

Author(s):

Zhihong Zhang ◽

Genzhou Zhang ◽

Zhonghao Zhang ◽

Guo Chen ◽

Yangbin Zeng ◽

...

Keyword(s):

Time Series ◽

Network Inference ◽

Time Series Data ◽

Generative Model ◽

Transfer Entropy ◽

Series Data ◽

Structural Network

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Finding opportunity windows in time series data using the sliding window technique: The case of stock exchanges

Econometrics ◽

10.15611/eada.2020.3.01 ◽

2020 ◽

Vol 24 (3) ◽

pp. 1-19

Author(s):

Necmi Gürsakal ◽

Fırat Melih Yilmaz ◽

Erginbay Uğurlu:

Keyword(s):

Time Series ◽

Time Series Data ◽

Stock Exchange ◽

Sliding Window ◽

Series Data ◽

Stock Exchanges ◽

Human Intelligence ◽

Window Technique

Data have shapes, and human intelligence and perception have to classify the forms of data to understand and interpret them. This article uses a sliding window technique and the main aim is to answer two questions. Is there an opportunity window in time series of stock exchange index? The second question is how to find a way to use the opportunity window if there is one. The authors defined the term opportunity window as a window that is generated in the sliding window technique and can be used for forecasting. In analysis, the study determined the different frequencies and explained how to evaluate opportunity windows embedded using time series data for the S&P 500, the DJIA, and the Russell 2000 indices. As a result, for the S&P 500 the last days of the patterns 0111, 1100, 0011; for the DJIA the last days of the patterns 0101, 1001, 0011; and finally for the Russell 2000, the last days of the patterns 0100, 1001, 1100 are opportunity windows for prediction

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Time-Series Data Analysis Using Sliding Window Based SVD for Motion Evaluation

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2017.p1240 ◽

2017 ◽

Vol 21 (7) ◽

pp. 1240-1250

Author(s):

Yinlai Jiang ◽

Isao Hayashi ◽

Shuoyu Wang ◽

Kenji Ishida ◽

◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Sliding Window ◽

Singular Values ◽

Singular Value ◽

Series Data ◽

Singular Vectors ◽

Walking Difficulty ◽

The Right ◽

Value Decomposition

A method based on singular value decomposition (SVD) is proposed for extracting features from motion time-series data observed with various sensing systems. Matrices consisting of the sliding window (SW) subsets of time-series data are decomposed, yielding singular vectors as the patterns of the motion, and the singular values as a scalar, by which the corresponding singular vectors describe the matrices.The sliding window based singular value decomposition was applied to analyze acceleration during walking. Three levels of walking difficulty were simulated by restricting the right knee joint in the measurement. The accelerations of the middles of the shanks and the back of the waist were measured and normalized before the SW-SVD was performed.The results showed that the first singular values inferred from the acceleration data of the restricted side (the right shank) significantly related to the increase of the restriction among all the subjects while there were no common trends in the singular values of the left shank and the waist. The SW-SVD was suggested to be a reliable method to evaluate walking disability. Furthermore, a 2D visualization tool is proposed to provide intuitive information about walking difficulty which can be used in walking rehabilitation to monitor recovery.

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