FORECASTING SHORT TERM FINANCIAL DATA

This article suggests an imperial real world problem technique for forecasting the financial time series data in order to solve real problems. The financial data are forecasted via ARIMA model in order to give some future suggestion about the case study. Some stock market data have been obtained from the department of land and survey in Jordan. Thus, this was aimed at implementing this model

EXISTING OUTLIER VALUES IN FINANCIAL DATA VIA WAVELET TRANSFORM

Outlier detection is one of the major problems of large datasets. Outliers have been detected using several methods such as the use of asymmetric winsorized mean. Al-Khazaleh et al. (2015) has proposed new methods of detecting the outlier values. This is achieved by combining the asymmetric winsorized mean with the famous spectral analysis function which is the Wavelet Transform (WT). Thus, this method is regarded as MTAWM. In this article, we will expand this work using the modern Wavelet function known as the Maximum Overlapping Wavelet Transform (MODWT). The results of the study shows that after comparing the new technique with the previous mentioned techniques using financial data from Amman Stock Exchange (ASE), the Maximum overlapping wavelet transform- asymmetric winsorized mean (MWAW) was considered the best method in outlier detections

FORECASTING SHORT TERM FINANCIAL DATA

This article suggests an imperial real world problem technique for forecasting the financial time series data in order to solve real problems. The financial data are forecasted via ARIMA model in order to give some future suggestion about the case study. Some stock market data have been obtained from the department of land and survey in Jordan. Thus, this was aimed at implementing this model

EXISTING OUTLIER VALUES IN FINANCIAL DATA VIA WAVELET TRANSFORM

Outlier detection is one of the major problems of large datasets. Outliers have been detected using several methods such as the use of asymmetric winsorized mean. Al-Khazaleh et al. (2015) has proposed new methods of detecting the outlier values. This is achieved by combining the asymmetric winsorized mean with the famous spectral analysis function which is the Wavelet Transform (WT). Thus, this method is regarded as MTAWM. In this article, we will expand this work using the modern Wavelet function known as the Maximum Overlapping Wavelet Transform (MODWT). The results of the study shows that after comparing the new technique with the previous mentioned techniques using financial data from Amman Stock Exchange (ASE), the Maximum overlapping wavelet transform- asymmetric winsorized mean (MWAW) was considered the best method in outlier detections

Applications of Artificial Intelligence in the Treatment of Behavioral and Mental Health Conditions

Introduction Artificial intelligence (AI) is the branch of science that studies and designs intelligent devices. For individuals unfamiliar with artificial intelligence, the concept of intelligent machines may bring up visions of attractive human-like computers or robots, like those described in science fiction. Others may consider AI technology to be mysterious machines limited to research facilities or a technical triumph that will come in the far future. Popular media accounts on the deployment of aerial drones, autonomous autos, or the potential dangers of developing super-intelligent technologies may have raised some broad awareness of the subject

Model tests on single batter piles subjected to lateral soil movement

A series of laboratory tests have been carried out to investigate the lateral response of battered piles under lateral soil movement. Model tests were carried out using instrumented rigid aluminium piles. The piles were embedded in homogeneous sand soil at batter angles &beta = 0°, ±10° and ±20° were subjected to two types of lateral soil movement profile. The results obtained from the study are presented in terms of the bending moment, shear force, soil reaction, pile rotation and lateral deflections along the length of the batter pile. The results of model tests on single vertical and batter piles under horizontal loads showed that the batter angle (&beta) significantly influenced the response of the batter piles. Regardless of the value of sand density, bending moment and deflection with batter angles &beta = +10° or positive batter piles were higher compared then vertical piles and negative batter piles

Forecasting Economic Growth and Movements with Wavelet Transform and ARIMA Model

This study uses historical data and modern statistical models to forecast future Gross Domestic Product (GDP) in Jordan. The Wavelet Transformation model (WT) and Autoregressive Integrated Moving Average (ARIMA) model were applied to the time series data and yielded a best-fitting result of (2,1,1) for estimating GDP between 2022-2031. The study concludes that GDP is expected to increase with a positive growth rate of around 3.22%, and recommends government agencies to monitor GDP, strengthen existing policies, and adopt necessary economic reforms to support growth. Additionally, the private sector is encouraged to enhance production tools to achieve economic growth that benefits all sectors of society

Are school and home environmental characteristics associated with oral health-related quality of life in Brazilian adolescents and young adults?

Objectives The aim of this study was to test the association of contextual school and home environmental characteristics and individual factors with oral health-related quality of life (OHRQoL) in a representative sample of Brazilian adolescents and young adults. Methods Individual-level data from 3854 fifteen- to nineteen-year-olds who participated in the Brazilian Oral Health Survey were pooled with contextual city-level data. The dependent variable was the frequency of impacts of oral disorders on daily performances (OIDP extent), as a measure of OHRQoL. Contextual school and home environmental characteristics were categorized into three equal groups according to tertiles of the contextual variable's scores (low, moderate and high). Individual demographic, socioeconomic and oral clinical measures were the covariates. The association between contextual and individual characteristics and OIDP extent was estimated using multilevel Poisson regression models. Results The mean of OIDP extent was 0.9 (standard error 0.1). Adolescents and young adults living in the cities with high levels of lack of security at school (RR 1.33; 95% CI=1.02-1.74), moderate levels of bullying at school (RR 1.56; 95% CI=1.20-2.03) and moderate levels of low maternal schooling (RR 1.43; 95% CI=1.06-1.92) had a higher mean OIDP extent. Male sex, higher age, skin colour, poor individual socioeconomic status and worse oral clinical measures were also associated with higher mean of OIDP extent. Conclusions Poor school and home environmental characteristics were independently associated with poor OHRQoL in individuals aged between 15 and 19 years. Our findings suggest the place where they study and the maternal level of education are meaningful aspects for their oral health