Influencing Factors and Prediction of Risk of Returning to Ecological Poverty in Liupan Mountain Region,China

China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.

Forecasting Stock Prices with an Integrated Approach Combining ARIMA and Machine Learning Techniques ARIMAML

Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.

Analysis and predication of tuberculosis registration rates in Henan Province, China: an exponential smoothing model study

Background The World Health Organization End TB Strategy meant that compared with 2015 baseline,the reduction in pulmonary tuberculosis(PTB)incidence should be 20 and 50%in 2020 and 2025,respectively.The case number of PTB in China accounted for 9%of the global total in 2018,which ranked the second high in the world.From 2007 to 2019,854672 active PTB cases were registered and treated in Henan Province,China.This study was to assess whether the WHO milestones could be achieved in Henan Province.Methods The active PTB numbers in Henan Province from 2007 to 2019,registered in Chinese Tuberculosis Information Management System were analyzed to predict the active PTB registration rates in 2020 and 2025,which is conductive to early response measures to ensure the achievement of the WHO milestones.The time series model was created by monthly active PTB registration rates from 2007 to 2016,and the optimal model was verified by data from 2017 to 2019.The Ljung-Box Q statistic was used to evaluate the model.The statistically significant level isα=0.05.Monthly active PTB registration rates and 95%confidence interval(CI)from 2020 to 2025 were predicted.Results High active PTB registration rates in March,April,May and June showed the seasonal variations.The exponential smoothing winter’s multiplication model was selected as the best-fitting model.The predicted values were approximately consistent with the observed ones from 2017 to 2019.The annual active PTB registration rates were predicted as 49.1(95%CI:36.2–62.0)per 100000 population and 34.4(95%CI:18.6–50.2)per 100000 population in 2020 and 2025,respectively.Compared with the active PTB registration rate in 2015,the reduction will reach 23.7%(95%CI,3.2–44.1%)and 46.8%(95%CI,21.4–72.1%)in 2020 and 2025,respectively.Conclusions The high active PTB registration rates in spring and early summer indicate that high risk of tuberculosis infection in late autumn and winter in Henan Province.Without regard to the CI,the first milestone of WHO End TB Strategy in 2020 will be achieved.However,the second milestone in 2025 will not be easily achieved unless there are early response measures in Henan Province,China.