scholarly journals MONEY LAUNDERING RISK IN DEVELOPING AND TRANSITIVE ECONOMIES: ANALYSIS OF CYCLIC COMPONENT OF TIME SERIES

2019 ◽  
Vol 20 (0) ◽  
pp. 492-508 ◽  
Author(s):  
Valentyna Levchenko ◽  
Anton Boyko ◽  
Victoria Bozhenko ◽  
Serhii Mynenko
Keyword(s):  
Time Series ◽  
Money Laundering ◽  
Medium Term ◽  
Developmental Patterns ◽  
Fourier Harmonic ◽  
Cyclic Component ◽  
Developed Economies ◽  
Economies In Transition ◽  
International Stability ◽  
Global Threat

Money laundering has become a global threat to the international stability and security, leading both to economic and social upheavals, and to an increase in terrorist threats. Therefore, an objective necessity arises for a more detailed study of the money laundering within the scope of its developmental patterns and time-dependent behaviour. The study mission is the development of a theoretical framework and methodological support for modelling the cyclic component of the money laundering risk. The correlation and regression are used for isolating the cyclic component. In turn, the Fourier harmonic analysis allows specifying the cyclic component. Additionally, we carried out a decomposition of time series, analysis of its volatility and persistence using the Hurst exponent. We determined the peaks, downturns and duration of the money laundering cycles in the developed economies and economies in transition, and established the possibility of predicting this process in the medium term. We proved the internationalization of the money laundering and the similarity of behaviour of trends that characterize it both for developed economies among themselves and between groups of countries. The further scientific research is needed within the framework of the imposition of trends in the development of the money laundering processes of some countries on others and the formation of international medium-term anti-fraud strategies.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Anti-money laundering law and policy as a double edged sword

2020 ◽  
Vol 23 (4) ◽  
pp. 899-912
Author(s):  
Norman Mugarura
Keyword(s):  
Money Laundering ◽  
Financial Institution ◽  
Business Environment ◽  
Security Issue ◽  
Patriot Act ◽  
Online Data ◽  
Content Type ◽  
Developed Economies ◽  
Secondary Sources ◽  
Financing Of Terrorism

Purpose Regulators have a duty to enforce anti-money laundering (AML) and countering financing of terrorism regulation. However, in doing so, they should not to be overzealous especially in carrying out investigations into suspicious money laundering transactions. This does not mean that oversight agencies should not carry out the required investigations with due diligence. This study aims to propose that banks cannot be allowed to operate in a lawless environment; however, there is a need ensure that businesses are able to operate with minimal regulatory interference. Design/methodology/approach Data was collected from primary and secondary sources such as Uganda’s Anti-Money laundering Act 2013 (amended 2017), Patriot Act 2001, Proceeds of Crime Act 2000 International legal instruments, case law, books, websites, journal papers, policy documents and scholarly debates and evaluated to foster the objectives of the paper accordingly. The paper has also been enriched by empirical experiences of countries in Europe, Africa and within countries on money-laundering regulation and its intricacies. There was a wealth of online data sources and in print, which were reviewed and internalised to foster the objectives for writing the book. Findings Regulation of businesses against money laundering and financing of terrorism imposes a heavy cost burden on poorer countries and should be funded by developed economies for some countries to easily operate desired International AML standards. It also needs to be noted that banks cannot be allowed to operate in a lawless business environment, which makes money laundering an international and national security issue. Originality/value The thesis of this paper was drawn from the author’s presentation to security agencies in Kampala in August 2019. In his presentation, the author opined that investigations into money-laundering offences should be triggered when a financial institution forms suspicions of potential money-laundering offences to have been committed. Some of the questions he sought to answer during the presentation was whether sharing information on “accountable persons or the regulated sector” in Uganda’s AML 2013 with newspapers before investigations are concluded does not amount to tipping off presumed money-laundering culprits? How should investigations be conducted?

scholarly journals The Medium-Term Impact of COVID-19 Lockdown on Referrals to Secondary Care Mental Health Services: A Controlled Interrupted Time Series Study

2020 ◽  
Vol 11 ◽  
Author(s):  
Shanquan Chen ◽  
Rui She ◽  
Pei Qin ◽  
Anne Kershenbaum ◽  
Emilio Fernandez-Egea ◽  
...  
Keyword(s):  
Mental Health ◽  
Time Series ◽  
Secondary Care ◽  
Interrupted Time Series ◽  
Service Planning ◽  
Chronic Obstructive ◽  
Medium Term ◽  
Health Crisis ◽  
Time Series Study ◽  
Series Study

To date, there is a paucity of information regarding the effect of COVID-19 or lockdown on mental disorders. We aimed to quantify the medium-term impact of lockdown on referrals to secondary care mental health clinical services. We conducted a controlled interrupted time series study using data from Cambridgeshire and Peterborough NHS Foundation Trust (CPFT), UK (catchment population ~0.86 million). The UK lockdown resulted in an instantaneous drop in mental health referrals but then a longer-term acceleration in the referral rate (by 1.21 referrals per day per day, 95% confidence interval [CI] 0.41–2.02). This acceleration was primarily for urgent or emergency referrals (acceleration 0.96, CI 0.39–1.54), including referrals to liaison psychiatry (0.68, CI 0.35–1.02) and mental health crisis teams (0.61, CI 0.20–1.02). The acceleration was significant for females (0.56, CI 0.04–1.08), males (0.64, CI 0.05–1.22), working-age adults (0.93, CI 0.42–1.43), people of White ethnicity (0.98, CI 0.32–1.65), those living alone (1.26, CI 0.52–2.00), and those who had pre-existing depression (0.78, CI 0.19–1.38), severe mental illness (0.67, CI 0.19–1.15), hypertension/cardiovascular/cerebrovascular disease (0.56, CI 0.24–0.89), personality disorders (0.32, CI 0.12–0.51), asthma/chronic obstructive pulmonary disease (0.28, CI 0.08–0.49), dyslipidemia (0.26, CI 0.04–0.47), anxiety (0.21, CI 0.08–0.34), substance misuse (0.21, CI 0.08–0.34), or reactions to severe stress (0.17, CI 0.01–0.32). No significant post-lockdown acceleration was observed for children/adolescents, older adults, people of ethnic minorities, married/cohabiting people, and those who had previous/pre-existing dementia, diabetes, cancer, eating disorder, a history of self-harm, or intellectual disability. This evidence may help service planning and policy-making, including preparation for any future lockdown in response to outbreaks.

scholarly journals Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series

2009 ◽  
Vol 2009 ◽  
pp. 1-21
Author(s):  
Sanjay L. Badjate ◽  
Sanjay V. Dudul
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Chaotic Time Series ◽  
Nonstationary Time Series ◽  
Medium Term ◽  
Short Term ◽  
Squared Error ◽  
Wide Range

Multistep ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in the recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multistep chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present some disadvantages when long-term prediction is required. In this paper focused time-lagged recurrent neural network (FTLRNN) model with gamma memory is developed for different prediction horizons. It is observed that this predictor performs remarkably well for short-term predictions as well as medium-term predictions. For coupled partial differential equations generated chaotic time series such as Mackey Glass and Duffing, FTLRNN-based predictor performs consistently well for different depths of predictions ranging from short term to long term, with only slight deterioration after k is increased beyond 50. For real-world highly complex and nonstationary time series like Sunspots and Laser, though the proposed predictor does perform reasonably for short term and medium-term predictions, its prediction ability drops for long term ahead prediction. However, still this is the best possible prediction results considering the facts that these are nonstationary time series. As a matter of fact, no other NN configuration can match the performance of FTLRNN model. The authors experimented the performance of this FTLRNN model on predicting the dynamic behavior of typical Chaotic Mackey-Glass time series, Duffing time series, and two real-time chaotic time series such as monthly sunspots and laser. Static multi layer perceptron (MLP) model is also attempted and compared against the proposed model on the performance measures like mean squared error (MSE), Normalized mean squared error (NMSE), and Correlation Coefficient (r). The standard back-propagation algorithm with momentum term has been used for both the models.

Kondratieff Cycles in Developed Economies

2015 ◽  
Vol 3 (5) ◽  
pp. 9-13 ◽  
Author(s):  
Басовский ◽  
Leonid Basovskiy
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Total Power ◽  
Economic Dynamics ◽  
Per Capita Gdp ◽  
Economic Cycles ◽  
Developed Economies ◽  
Kondratieff Cycles ◽  
Per Capita ◽  
The 19Th Century

The objective of this paper is to identify Kondratieff cycles in the developed economies. Time series spectral analysis of real per capita GDP of the developed countries and Brazil is performed. Also studied are time series for the period from the 19th century to 2008. As a result Kondratieff cycles (waves) are found out in the economic dynamics of all the countries surveyed, except for Finland. The power of Kondratieff cycles in the economic dynamics is estimated to fall in the range of 23 to 61% of the total power of all economic cycles with the periods of 2 to 100 years. The Kondratieff cycles can be found in a number of economies in the period of 19th — 20th centuries. It allows to distinguish the three moderntime Kondratieff waves in the said countries and to evaluate productivity of the fourth, the fifth and the sixth technological modes in their economies. However in a number of countries the Kondratieff cycles show up only in the 20th century. So for these countries only one or two modern Kondratieff waves can be clearly identified, making it possible to evaluate productivity of only the fifth and the sixth technological modes in their economies.

Wages and On-Costs in Australian Industries: 1968-69 to 1985-86

1988 ◽  
Vol 30 (2) ◽  
pp. 294-310 ◽  
Author(s):  
Peter B. Dixon ◽  
David A. Prentice ◽  
Lynne S. Williams
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Data Sources ◽  
Series Data ◽  
Medium Term ◽  
Cost Share ◽  
Labour Costs ◽  
The Relationship

In the study summarized here, estimates were made of the costs of employing labour in Australian industries for the years 1968-69 to 1985-86. The estimates include wages and five types of on-costs. They imply that the on-cost share in labour costs increased sharply in each industry over the period 1968-69 to 1974-75. Since 1974-75, on-cost shares have continued to grow but at moderate rates. The estimates rely on a wide variety of data sources and involve numerous interpolations and extrapolations. They are suitable for the analysis of medium-term trends rather than annual movements. It is to be hoped that the ABS continues its surveys of labour costs started in 1987. These surveys would then provide satisfactory time-series data for investigating the relationship between labour costs and other variables such as employment and inflation. They would also obviate the need for further studies of the type reported in this paper.

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