Unsupervised Fraud Detection in Environmental Time Series Data

Fraud detection technology is an important method to ensure financial security. It is necessary to develop explainable fraud detection methods to express significant causality for participants in the transaction. The main contribution of our work is to propose an explainable classification method in the framework of multiple instance learning (MIL), which incorporates the AP clustering method in the self-training LSTM model to obtain a clear explanation. Based on a real-world dataset and a simulated dataset, we conducted two comparative studies to evaluate the effectiveness of the proposed method. Experimental results show that our proposed method achieves the similar predictive performance as the state-of-art method, while our method can generate clear causal explanations for a few labeled time series data. The significance of the research work is that financial institutions can use this method to efficiently identify fraudulent behaviors and easily give reasons for rejecting transactions so as to reduce fraud losses and management costs.

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A simulation study on methods for modelling lagged associations in environmental time series data

ISEE Conference Abstracts ◽

10.1289/isee.2016.3992 ◽

2016 ◽

Vol 2016 (1) ◽

Author(s):

Antonio Gasparrini*

Keyword(s):

Time Series ◽

Simulation Study ◽

Time Series Data ◽

Series Data ◽

Environmental Time Series

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Accessing environmental time series data in R from Sensor Observation Services with ease

10.5194/egusphere-egu2020-19453 ◽

2020 ◽

Author(s):

Daniel Nüst ◽

Eike H. Jürrens ◽

Benedikt Gräler ◽

Simon Jirka

Keyword(s):

Time Series ◽

Time Series Data ◽

Data Access ◽

R Package ◽

Series Data ◽

Observation Data ◽

Software Environment ◽

Technical Standards ◽

Seamless Integration ◽

Environmental Time Series

Time series data of in-situ measurements is the key to many environmental studies. The first challenge in any analysis typically arises when the data needs to be imported into the analysis framework. Standardisation is one way to lower this burden. Unfortunately, relevant interoperability standards might be challenging for non-IT experts as long as they are not dealt with behind the scenes of a client application. One standard to provide access to environmental time series data is the Sensor Observation Service (SOS, ) specification published by the Open Geospatial Consortium (OGC). SOS instances are currently used in a broad range of applications such as hydrology, air quality monitoring, and ocean sciences. Data sets provided via an SOS interface can be found around the globe from Europe to New Zealand.The R package sos4R (N&#252;st et al., 2011) is an extension package for the R environment for statistical computing and visualization (), which has been demonstrated a a powerful tools for conducting and communicating geospatial research (cf. Pebesma et al., 2012; ). sos4R comprises a client that can connect to an SOS server. The user can use it to query data from SOS instances using simple R function calls. It provides a convenience layer for R users to integrate observation data from data access servers compliant with the SOS standard without any knowledge about the underlying technical standards. To further improve the usability for non-SOS experts, a recent update to sos4R includes a set of wrapper functions, which remove complexity and technical language specific to OGC specifications. This update also features specific consideration of the OGC SOS 2.0 Hydrology Profile and thereby opens up a new scientific domain.In our presentation we illustrate use cases and examples building upon sos4R easing the access of time series data in an R and Shiny () context. We demonstrate how the abstraction provided in the client library makes sensor observation data for accessible and further show how sos4R allows the seamless integration of distributed observations data, i.e., across organisational boundaries, into transparent and reproducible data analysis workflows.ReferencesN&#252;st D., Stasch C., Pebesma E. (2011) Connecting R to the Sensor Web. In: Geertman S., Reinhardt W., Toppen F. (eds) Advancing Geoinformation Science for a Changing World. Lecture Notes in Geoinformation and Cartography, Springer. Pebesma, E., N&#252;st, D., & Bivand, R. (2012). The R software environment in reproducible geoscientific research. Eos, Transactions American Geophysical Union, 93(16), 163&#8211;163.

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Modelling Lagged Associations in Environmental Time Series Data

Epidemiology ◽

10.1097/ede.0000000000000533 ◽

2016 ◽

Vol 27 (6) ◽

pp. 835-842 ◽

Cited By ~ 26

Author(s):

Antonio Gasparrini

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Environmental Time Series

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Selection Bias and Confounding in Case-Crossover Analyses of Environmental Time-Series Data

Epidemiology ◽

10.1097/00001648-200111000-00013 ◽

2001 ◽

Vol 12 (6) ◽

pp. 654-661 ◽

Cited By ~ 84

Author(s):

Thomas F. Bateson ◽

Joel Schwartz

Keyword(s):

Time Series ◽

Selection Bias ◽

Time Series Data ◽

Series Data ◽

Case Crossover ◽

Environmental Time Series

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

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

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

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

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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