Design of Multi-Level Abnormal Detection System Suitable for Time-Series Data

2016 ◽  
Vol 16 (6) ◽  
pp. 1-7
Author(s):  
Moon-Chang Chae ◽  
◽  
Hyeok Lim ◽  
Namhi Kang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Detection System ◽  
Series Data ◽  
Abnormal Detection ◽  
Multi Level

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

2016 ◽  
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 ◽  
Series Data ◽  
Gap Filling ◽  
2011 Tohoku Tsunami ◽  
Overall Performance ◽  
Tsunami Signal ◽  
Tohoku Tsunami

Abstract. Timely detection of tsunamis with water-level records is a critical but logistically challenging task because of outliers and gaps. We propose a tsunami arrival time detection system (TADS) that can be applied to discontinuous time-series data with outliers. TADS consists of three major algorithms that are designed to update at every new data acquisition: outlier detection, gap-filling, and tsunami detection. To detect a tsunami from a record containing outliers and gaps, we propose the concept of the event period. In this study, we applied this concept in our test of the TADS at the Ulleung-do surge gauge located in the East Sea. We calibrated the thresholds to identify tsunami arrivals based on the 2011 Tohoku tsunami, and the results show that the overall performance of TADS is effective at detecting a small tsunami signal superimposed on both an outlier and gap.

scholarly journals Analysis and Prediction of Alerts in Perimeter Intrusion Detection System

2020 ◽  
Vol 70 (6) ◽  
pp. 619-625
Author(s):  
Rizul Aggarwal ◽  
Anjali Goswami ◽  
Jitender Kumar ◽  
Gwyneth Abdiel Chullai
Keyword(s):  
Time Series ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Time Series Data ◽  
Detection System ◽  
Series Data ◽  
Support Vector ◽  
Short Term ◽  
Term Prediction

Perimeter surveillance systems play an important role in the safety and security of the armed forces. These systems tend to generate alerts in advent of anomalous situations, which require human intervention. The challenge is the generation of false alerts or alert flooding which makes these systems inefficient. In this paper, we focus on short-term as well as long-term prediction of alerts in the perimeter intrusion detection system. We have explored the dependent and independent aspects of the alert data generated over a period of time. Short-term prediction is realized by exploiting the independent aspect of data by narrowing it down to a time-series problem. Time-series analysis is performed by extracting the statistical information from the historical alert data. A dual-stage approach is employed for analyzing the time-series data and support vector regression is used as the regression technique. It is helpful to predict the number of alerts for the nth hour. Additionally, to understand the dependent aspect, we have investigated that the deployment environment has an impact on the alerts generated. Long-term predictions are made by extracting the features based on the deployment environment and training the dataset using different regression models. Also, we have compared the predicted and expected alerts to recognize anomalous behaviour. This will help in realizing the situations of alert flooding over the potential threat.

Excretion Detection System with Gas Sensor – Proposal and Verification of Algorithm Based on Time-Series Clustering –

2017 ◽  
Vol 29 (2) ◽  
pp. 353-363 ◽  
Author(s):  
Yoshimi Ui ◽  
◽  
Yutaka Akiba ◽  
Shohei Sugano ◽  
Ryosuke Imai ◽  
...  
Keyword(s):  
Time Series ◽  
Gas Sensor ◽  
Gas Sensors ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Detection System ◽  
Detection Algorithm ◽  
Sensor Data ◽  
Series Data ◽  
Processing Unit

[abstFig src='/00290002/09.jpg' width='300' text='Standard Lifilm configuration' ] In this study, we propose an excretion detection system, Lifi, which does not require sensors inside diapers, and we verify its capabilities. It consists of a sheet with strategically placed air intakes, a set of gas sensors, and a processing unit with a newly developed excretion detection algorithm. The gas sensor detects chemicals with odor in the excrement, such as hydrogen sulfide and urea. The time-series data from the gas sensor was used for the detection of not only excretion, but also of the presence/absence of the cared person on the bed. We examined two algorithms, one with a simple threshold and another based on the clustering of sensor data, obtained using the<span class=”bold”>k</span>-means method. The results from both algorithms were satisfactory and similar, once the algorithms were customized for each cared person. However, we adopted the clustering algorithm because it possesses a higher level of flexibility that can be explored and exploited. Lifi was conceived from an overwhelming and serious desire of caretakers to discover the excretion of bed-ridden cared persons, without opening their diapers. We believe that Lifi, along with the clustering algorithm, can help caretakers in this regard.

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.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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