• Convergence Security Journal
• /
• v.22 no.3
• /
• pp.79-86
• /
• 2022

Voice phishing is a cyber crime in which fake financial institutions, the Public Prosecutor's Office, and the National Police Agency are impersonated to find out an individual's Certification number and credit card number or withdraw a deposit. Recently, voice phishing has been carried out in a subtle and secret way. Analyzing the trend of voice phishing that occurred in '18~'21, it was found that there is a seasonality that occurs rapidly at a time when the movement of money is intensifying in the trend of voice phishing, giving ambiguity to time series analysis. In this research, we adjusted seasonality using the X-12 seasonality adjustment methodology for accurate prediction of voice phishing occurrence trends, and predicted the occurrence of voice phishing in 2022 using the ARIMA model.

• Communications for Statistical Applications and Methods
• /
• v.7 no.2
• /
• pp.513-523
• /
• 2000

The X-12-ARIMA program was utilized on the analysis of the time series trend on 76 Korean industrial activities data in order to ensure that the trading day effect adjustment as well as the seasonal effect adjustment is needed to extract the fundamental trend-cycle factors from various economic time series data. The trading day effect is strongly correlated with the activity of production and shipping but not with the activity of inventory. Furthermore, the industrial activities were classified with respect to the sensitivity on the tranding day effect.

• Journal of Korean Society for Atmospheric Environment
• /
• v.4 no.2
• /
• pp.72-81
• /
• 1988

The typical ARIMA model which was developed by Box and Jenkins, was applied to the monthly $SO_2$ data collected at Seoungsoo and Oryudong in metropolitan area over five years, 1982 to 1986. To find out the changing pattern of $SO_2$ concentration, autocorrelation and partial autocorrelation analysis were undertaken. The three steps of time series model building were followed and the residual series was found to be a random white noise. The results of this study is summarized as follows. 1) The monthly $SO_2$ series was found to be a non-stationary series which which has a periodicity of 12 months. After eliminating the periodicity by differencing, the monthly $SO_2$ series became a stationary series. 2) The ARIMA seasonal model of the $SO_2$ was determined to be ARIMA $(1, 0, 0)(0, 1, 0,)_{12}$ model. 3) The model equations based on the prediction were: for Seoungsoodong: $Y_t = 0.5214Y_{t-1} + Y_{t-12} - 0.5214Y_{t-13} + a_t$ for Oryudong: $Y_t = 0.8549Y_{t-1} + Y_{t-12} - 0.8549Y_{t-13} + a_t$ 4) The validity of the model identified was checked by compairing the measured $SO_2$ values and one-month-ahead predicted values. The result of correlation and regression analysis is as follows. Seoungsoodong: $Y = 0.8710X + 0.0062 r = 0.8768$ Oryudong : $Y = 0.8758X + 0.0073 r = 0.9512$

• Proceedings of the KSR Conference
• /
• 2001.10a
• /
• pp.111-116
• /
• 2001

This paper introduces how to adopt the X-12-ARIMA to decompose the railway passenger demand of the Korea National Railroad Especially, selecting on proper filters is focused. The trend filter is identical to the low pass filter in the signal Processing field, and so the seasonal filter is to band pass filter too. Some considerations, selecting a filter, are provided from the view-point of the spectrum analysis. The technique introduced in this paper will be adopted to the project that is to develope the forecasting system of Korea railway passenger demand which is a part of the high speed rail information system.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.6
• /
• pp.109-116
• /
• 2020

The study examines the nexus between financial development and economic growth in India during Q1: 1996 to Q3: 2018. This study employs time-series data of real GDP and ratio of broad money to GDP as a proxy for economic and financial development, respectively. The data are obtained from RBI database on the Indian economy. All variables are seasonally adjusted using X12-arima technique and expressed in natural logarithm form. Non-linear Autoregressive Distributed Lag (NARDL) bound test has been used to check for cointegrating relationship of these two variables. Empirical findings suggest that, unlike in the short run, in the long run financial development does impact economic growth positively. Further, a symmetric effect of positive and negative components of financial development is found for the Indian economy, whereas the effect of control variable like exchange rate and trade openness is in consonance with common economic intuition. Exchange rate is in consonance with intuitive economic logic that a fall in exchange rate makes exports cheaper and increases the quantity of export, which improves the balance of payment and leads to a rise in aggregate demand, hence improves economic growth. This paper contributes to the existing literature on India by breaking down financial indicator into positive and negative components to examine the finance-growth relationship.

• Journal of Labour Economics
• /
• v.39 no.1
• /
• pp.1-31
• /
• 2016

This paper investigates the cyclical behavior of UI benefit take-up rate, the share of unemployed persons who are eligible for job seekers' allowances(JSA) and actually receive them. Using Korea's Employment Insurance DB, it also identifies the factors linked to the decision to take up job seekers' allowances. The results show that the take-up rate is countercyclical and leads both unemployment rate and Coincident Composite Index cyclical component by 6 months and is positively correlated with replacement rate and benefit duration, suggesting that extending benefit duration and raising benefit level can boost benefit claims to increase take-up rates in Korea.