scholarly journals Correction to: Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function

2021 ◽  
Author(s):  
Adil Aslam Mir ◽  
Fatih Vehbi Çelebi ◽  
Muhammad Rafique ◽  
M. R. I. Faruque ◽  
Mayeen Uddin Khandaker ◽  
...  
Keyword(s):  
Time Series ◽  
Earthquake Prediction ◽  
Time Series Data ◽  
Series Data ◽  
Anomaly Classification

Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function

2021 ◽  
Author(s):  
Adil Aslam Mir ◽  
Fatih Vehbi Çelebi ◽  
Muhammad Rafique ◽  
M. R. I. Faruque ◽  
Mayeen Uddin Khandaker ◽  
...  
Keyword(s):  
Time Series ◽  
Earthquake Prediction ◽  
Time Series Data ◽  
Series Data ◽  
Anomaly Classification

Descriptive analysis and earthquake prediction using boxplot interpretation of soil radon time series data

2019 ◽  
Vol 154 ◽  
pp. 108861 ◽  
Author(s):  
Aleem Dad Khan Tareen ◽  
Malik Sajjad Ahmed Nadeem ◽  
Kimberlee Jane Kearfott ◽  
Kamran Abbas ◽  
Muhammad Asim Khawaja ◽  
...  
Keyword(s):  
Time Series ◽  
Earthquake Prediction ◽  
Time Series Data ◽  
Descriptive Analysis ◽  
Series Data ◽  
Soil Radon

scholarly journals A Novel Deep Learning Approach for Anomaly Detection of Time Series Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiwei Ji ◽  
Jiaheng Gong ◽  
Jiarui Feng
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Short Term Memory ◽  
Training Data ◽  
Series Data ◽  
Real Time System ◽  
Statistical Strategy ◽  
Anomaly Classification ◽  
Real World Datasets

Anomalies in time series, also called “discord,” are the abnormal subsequences. The occurrence of anomalies in time series may indicate that some faults or disease will occur soon. Therefore, development of novel computational approaches for anomaly detection (discord search) in time series is of great significance for state monitoring and early warning of real-time system. Previous studies show that many algorithms were successfully developed and were used for anomaly classification, e.g., health monitoring, traffic detection, and intrusion detection. However, the anomaly detection of time series was not well studied. In this paper, we proposed a long short-term memory- (LSTM-) based anomaly detection method (LSTMAD) for discord search from univariate time series data. LSTMAD learns the structural features from normal (nonanomalous) training data and then performs anomaly detection via a statistical strategy based on the prediction error for observed data. In our experimental evaluation using public ECG datasets and real-world datasets, LSTMAD detects anomalies more accurately than other existing approaches in comparison.

Earthquake Prediction Research Based on the Mining in Time Series of Groundwater Temperature

2012 ◽  
Vol 532-533 ◽  
pp. 1016-1020 ◽  
Author(s):  
Jian Zhang ◽  
Yi Lin Lu ◽  
Shao Chun Wu
Keyword(s):  
Time Series ◽  
Earthquake Prediction ◽  
Time Series Data ◽  
Sequence Data ◽  
Piecewise Linear ◽  
Extreme Points ◽  
Linear Representation ◽  
Series Data ◽  
Groundwater Temperature ◽  
Symbol Sequence

Earthquake prediction has always been an extremely important and difficult research topic. A road map was proposed in this paper to capture useful information for earthquake prediction by exploring the time sequence data of groundwater temperature. Firstly, the triangle extreme points and the trend turning points are employed for the piecewise linear representation of the time series data. Then the segmentation is classified and symbolized by slope, and symbol sequence is simplified further according to the simplification rules. Finally, the earthquake catalogue data and the symbol sequence are jointly preprocessed with a new method to form transaction-like data, which then be treated by association analysis to extract earthquake prediction knowledge. The results of experiment show that this processing flow is an effective way to provide valuable information about earthquake prediction.

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.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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