• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.3
• /
• pp.165-170
• /
• 2021

This paper examines the long-run relationship between financial development and economic growth. The effective function of financial development is crucial to promote the economic development of the country. To achieve the objective, this study used Gross Domestic Product as a dependent variable and Credit to The Private Sector, Ratio of the Gross Fixed Capital Formation to GDP, Trade, Consumer Price Index and Labour Force as an independent variable. Augmented Dickey-Fuller test statistic (ADF) to check the stationary. Bounds test for cointegration and Auto-Regressive Distributed Lag Models (ARDL) are used to check cointegrating relationship amongst the variables and causality between financial development and economic growth. Moreover, the Model selection method is Akaike Info Criterion (AIC). This result demonstrates that the labor force and trade hold a significantly negative relationship with economic growth. Nevertheless, inflation, Credit to The Private Sector, and Ratio of the Gross Fixed Capital Formation to GDP show a significantly positive relationship with economic growth. Therefore, there is a statistically significant relationship between Financial Development and Economic growth in Sri Lanka and the Sri Lankan government should reform its trade policies.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.2
• /
• pp.111-122
• /
• 2021

The main purpose of the present research is to analyze the effects of trade, financial globalization, and technological progress on income inequality in the Indian economy over the period from 1982 to 2018. For this purpose, the study uses economic growth, financial globalization, trade openness, technological development, and economic inequality variables with appropriate proxies. The study employs the Auto Regressive Distributed Lag (ARDL) approach to co-integration and VECM based Granger causality approach to estimate both the short-run and long-run relationship and causality among variables. Using the ARDL bounds test, the study finds a long-run co-integrating relationship existing among the variables in the model. The study confirms the existence of a positive and significant impact of technological progress on income inequality. Further, globalization's limited impact reflects two offsetting tendencies; trade globalization is associated with a reduction in income inequality, while financial globalization is related to an increase in inequality. The results of VECM based Granger causality approach further confirm that technological progress, trade, and financial globalization causes income inequality both directly and indirectly through economic growth and inflation. In case of India, the results of this research can significantly facilitate stakeholders and policymakers in devising policies towards effective globalization and technological innovation for inclusive growth.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.339-347
• /
• 2021

This study aims to forecast the exchange rate by a combination of different models as proposed by Poon and Granger (2003). For this purpose, we include three univariate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Pakistani Rupee against the US dollar by a combination of different forecasting techniques. The observations from M1 2020 to M12 2020 are held back for in-sample forecasting. The models are then assessed through equal weightage and var-cor methods. Our results suggest that NARDL outperforms all individual time series models in terms of forecasting the exchange rate. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models with the lowest MAPE value of 0.612 suggesting that the Pakistani Rupee exchange rate against the US Dollar is dependent upon the macro-economic fundamentals and recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting, as stated by Poon and Granger (2003).

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.113-124
• /
• 2021

The study investigates how financial markets respond to a shock to tone and semantic similarity of the Bank of Thailand press releases. The techniques in natural language processing are employed to quantify the tone and the semantic similarity of 69 press releases from 2010 to 2018. The corpus of the press releases is accessible to the general public. Stock market returns and bond yields are measured by logged return on SET50 and short-term and long-term government bonds, respectively. Data are daily from January 4, 2010, to August 8, 2019. The study uses the Structural Vector Auto Regressive model (SVAR) to analyze the effects of unanticipated and temporary shocks to the tone and the semantic similarity on bond yields and stock market returns. Impulse response functions are also constructed for the analysis. The results show that 1-month, 3-month, 6-month and 1-year bond yields significantly increase in response to a positive shock to the tone of press releases and 1-month, 3-month, 6-month, 1-year and 25-year bond yields significantly increase in response to a positive shock to the semantic similarity. Interestingly, stock market returns obtained from the SET50 index insignificantly respond to the shocks from the tone and the semantic similarity of the press releases.

• International Journal of Computer Science & Network Security
• /
• v.22 no.1
• /
• pp.225-233
• /
• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.2
• /
• pp.467-479
• /
• 2022

Working memory (WM), which plays a vital role in daily activities, is a memory system that temporarily stores and processes information when people are engaged in complex cognitive activities. The influence of music on WM has been widely studied. In this work, we conducted a series of n-back memory experiments with different task difficulties and multiple trials on 14 subjects under the condition of no music and Alpha wave leading music. The analysis of behavioral data show that the change of music condition has significant effect on the accuracy and time of memory reaction (p<0.01), both of which are improved after the stimulation of Alpha wave music. Behavioral results also suggest that short-term training has no significant impact on working memory. In the further analysis of electrophysiology (EEG) data recorded in the experiment, auto-regressive (AR) model is employed to extract features, after which an average classification accuracy of 82.9% is achieved with support vector machine (SVM) classifier in distinguishing between before and after WM enhancement. The above findings indicate that Alpha wave leading music can improve WM, and the combination of AR model and SVM classifier is effective in detecting the brain activity changes resulting from music stimulation.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.2
• /
• pp.284-294
• /
• 2018

Statistical methods have been widely used to estimate the remaining battery runtime of mobile smart devices, such as smart phones, smart gears, tablets, and etc. However, existing work available in the literature only considers a particular statistical method. Thus, it is difficult to determine whether statistical methods are applicable to estimating thr remaining battery runtime of mobile devices or not. In this paper, we evaluated the performance of statistical methods applicable to estimating the remaining battery runtime of mobile smart devices. The statistical estimation methods evaluated in this paper are as follows: simple and moving average, linear regression, multivariate adaptive regression splines, auto regressive, polynomial curve fitting, and double and triple exponential smoothing methods. Research results presented in this paper give valuable data of insight to IT engineers who are willing to deploy statistical methods on estimating the remaining battery runtime of mobile smart devices.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10b
• /
• pp.1659-1663
• /
• 1991

A new control design methodology is presented here which is based on a nonlinear time-series reference model. It is indicated by highly nonlinear simulations that such designs successfully stabilize troublesome aircraft maneuvers undergoing large changes in angle of attack as well as large electric power transients due to line faults. In both applications, the nonlinear controller was significantly better than the corresponding linear adaptive controller. For the electric power network, a flexible a.c. transmission system (FACTS) with series capacitor power feedback control is studied. A bilinear auto-regressive moving average (BARMA) reference model is identified from system data and the feedback control manipulated according to a desired reference state. The control is optimized according to a predictive one-step quadratic performance index (J). A similar algorithm is derived for control of rapid changes in aircraft angle of attack over a normally unstable flight regime. In the latter case, however, a generalization of a bilinear time-series model reference includes quadratic and cubic terms in angle of attack. These applications are typical of the numerous plants for which nonlinear adaptive control has the potential to provide significant performance improvements. For aircraft control, significant maneuverability gains can provide safer transportation under large windshear disturbances as well as tactical advantages. For FACTS, there is the potential for significant increase in admissible electric power transmission over available transmission lines along with energy conservation. Electric power systems are inherently nonlinear for significant transient variations from synchronism such as may result for large fault disturbances. In such cases, traditional linear controllers may not stabilize the swing (in rotor angle) without inefficient energy wasting strategies to shed loads, etc. Fortunately, the advent of power electronics (e.g., high-speed thyristors) admits the possibility of adaptive control by means of FACTS. Line admittance manipulation seems to be an effective means to achieve stabilization and high efficiency for such FACTS. This results in parametric (or multiplicative) control of a highly nonlinear plant.

• Journal of Korea Water Resources Association
• /
• v.39 no.8 s.169
• /
• pp.691-702
• /
• 2006

This study introduced a method to evaluate the probability of a specific area to be affected by a drought of a given severity and shows Its potential for investigating agricultural drought characteristics. The method was applied to South Korea as a case study. The proposed procedure included Standardized Precipitation Index(SPI) time series, which were linearly transformed by the Empirical Orthogonal Functions(EOF) method. These EOFs were extended temporally with AutoRegressive Moving Average(ARMA) method and spatially with Kriging method. By performing these simulations, long time series of SPI can be simulated for each designed grid cell in whole area. The probability distribution functions of the area covered by a drought and the drought severity are then derived and combined to produce drought severity-area-frequency(SAF) curves.

• Water for future
• /
• v.15 no.3
• /
• pp.23-36
• /
• 1982

The stochastic variations and structures of time series data on water quality were examined by employing the techniques of autocorrelation function, variance spectrum, Fourier series, autoregressive model and ARIMA model. These time series included hourly and daily observation on DO, turbidity, conductivity pH and water temperature. The measurement was made by automatic recording instrument at Noryangjin and Dook-do located in the downstream part of Han River during 1975 and 1976. Hourly water quality time series varied with the dominant 24-hour periodicity, and the 12-hour periodicity was also observed. An important factor affecting 24-hour periodic variation of DO is believed to be photosynthesis by algae. These phenomena might be attributable to periodic discharges of municipal sewage. Noryangjin site showed the more distinct 12-hour periodicity than Dook-do site did, and tidal effect might be responsible for the difference. The water quality, as measured by DO and turbidity, was better in the afternoon compared with the quality in the morning. This change can be explained by the periodic variation of DO, temperature and the amount of municipal wewage discharge. It was also observed that the water temperature at Noryangjin was higher than the temperature at Dook-do. This difference might have been caused by the pollutants that were added to the section between two sites. The correlation coefficients between some of the variables were fairly high. For example, the coefficient was -0.88 between DO and water temperature, 0.75 between turbidity and river flow, and 0.957 between water temperature and air temperature. The lag time of heat transfer from the air to the water was estimated as 24 days. The first order auto-regressive model was appropriate for explaning standardized hourly DO time series. The ARIMA model of (1, 0, 0) type provided relatively satisfactory results for daily DO time series after the removal of significant harmonic value.