Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing

In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.

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.

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.

2020—2025年广东省医疗机构床位需求预测

目的预测2020—2025年广东省医疗机构的床位需求总量。方法基于卫生服务需求法与Holt双参数指数平滑模型,结合年龄别人口数据预测床位需求。结果2025年,广东省住院人数为2425.11万人,床位需求数为70.04万张,每千常住人口床位需求数为5.63张、每千常住人口拥有床位数5.55张,供需比例为98.58%。预测模型的平均百分误差为1.63%(标准差=±1.90%,均方根误差=20.63)。结论结合人口的年龄结构进行预测结果更稳定、误差更小。2020年,广东省的床位配置量基本能满足床位需求,供需较为平衡。但2024年床位需求将超过床位配置总量。未来,广东省应加大床位资源的投入力度,提高基层卫生机构的床位利用率,全面落实分级诊疗制度。

2012年伊朗人造板需求量预测

Considering increasing rate of Iran population and consumption of wood panels, the authors investigated the per capita consump- tion of wood panels during the years from 1997 to 2007. The exponential smoothing method was used to obtain a per capita consumption panern of wood panels in lran for estimating demand of wood panels by the year of 2012. Results show that the consumption of particleboard, fiberboard, and medium density fiberboard in lran will increase by 33%, 72% and 107 %o, respectively, by the year of 2012; however, the consumption of plywood will increase only by 7% by 2012. The deficient amount of wood panels in Iran is estimated over 1400 000 m^3. The results of this study provide the technique reference for planners of wood panel industries in Iran in capital investment decisions.

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability.Despite the scientific interest suggested by such assumptions,the relationships between descriptive time series features(e.g.,temporal dependence,entropy,seasonality,trend and linearity features)and actual time series forecastability(quantified by issuing and assessing forecasts for the past)are scarcely studied and quantified in the literature.In this work,we aim to fill in this gap by investigating such relationships,and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns.To this end,we follow a systematic framework bringing together a variety of–mostly new for hydrology–concepts and methods,including 57 descriptive features and nine seasonal time series forecasting methods(i.e.,one simple,five exponential smoothing,two state space and one automated autoregressive fractionally integrated moving average methods).We apply this framework to three global datasets originating from the larger Global Historical Climatology Network(GHCN)and Global Streamflow Indices and Metadata(GSIM)archives.As these datasets comprise over 13,000 monthly temperature,precipitation and river flow time series from several continents and hydroclimatic regimes,they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale.We first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency,while the simple method is shown to be mostly useful in identifying its lower limit.We then demonstrate that the assessed forecastability is strongly related to several descriptive features,including seasonality,entropy,(partial)autocorrelation,stability,(non)linearity,spikiness and heterogeneity features,among others.We further(i)show that,if such descriptive information is available for a monthly hydroclimatic time series,we can even foretell the quality of its future forecasts with a considerable degree of confidence,and(ii)rank the features according to their efficiency in explaining and foretelling forecastability.We believe that the obtained rankings are of key importance for understanding forecastability.Spatial forecastability patterns are also revealed through our experiments,with East Asia(Europe)being characterized by larger(smaller)monthly temperature time series forecastability and the Indian subcontinent(Australia)being characterized by larger(smaller)monthly precipitation time series forecastability,compared to other continental-scale regions,and less notable differences characterizing monthly river flow from continent to continent.A comprehensive interpretation of such patters through massive feature extraction and feature-based time series clustering is shown to be possible.Indeed,continental-scale regions characterized by different degrees of forecastability are also attributed to different clusters or mixtures of clusters(because of their essential differences in terms of descriptive features).

Predicting LTE Throughput Using Traffic Time Series

Throughput prediction is essential for congestion control and LTE network management. In this paper, the autoregressive integrated moving average （ARIMA） model and exponential smoothing model are used to predict the throughput in a single cell and whole region in an LTE network. The experimental results show that these two models perform differently in both scenarios. The ARIMA model is better than the exponential smoothing model for predicting throughput on weekdays in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput on weekends in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput in a single cell. In these two LTE network scenarios, throughput prediction based on traffic time series leads to more efficient resource management and better QoS.

Forecasting Spare Parts Demand Using Statistical Analysis

Spare parts are very essential in most industrial companies. They are characterized by their large number and their high impact on the companies’ operations whenever needed. Therefore companies tend to analyze their spare parts demand and try to estimate their future consumption. Nevertheless, they face difficulties in figuring out an optimal forecasting method that deals with the lumpy and intermittent demand of spare parts. In this paper, we performed a comparison between five forecasting methods based on three statistical tools;Mean squared error (MSE), mean absolute deviation (MAD) and mean error (ME), where the results showed close performance for all the methods associated with their optimal parameters and the frequency of the spare part demand. Therefore, we proposed to compare all the methods based on the tracking signal with the objective of minimizing the average number of out of controls. This approach was tested in a comparative study at a local paper mill company. Our findings showed that the application of the tracking signal approach helps companies to better select the optimal forecasting method and reduce forecast errors.

Oil-Price Forecasting Based on Various Univariate Time-Series Models

Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.

An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease.In addition to forecasting the occurrences of conjunctivitis incidences,the proposed model also improves performance by using the ensemble model.Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019.Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets.A stacked generalization ensemble model based on Auto-ARIMA(Autoregressive Integrated Moving Average),NNAR(Neural Network Autoregression),ETS(Exponential Smoothing),HW(Holt Winter)is proposed and applied on the dataset.Predictive analysis is conducted on the collected dataset of conjunctivitis disease,and further compared for different performance measures.The result shows that the RMSE(Root Mean Square Error),MAE(Mean Absolute Error),MAPE(Mean Absolute Percentage Error),ACF1(Auto Correlation Function)of the proposed ensemble is decreased significantly.Considering the RMSE,for instance,error values are reduced by 39.23%,9.13%,20.42%,and 17.13%in comparison to Auto-ARIMA,NAR,ETS,and HW model respectively.This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA,NAR,ETS,and HW model applied discretely.

Flood Mapping in Mozambique Using Copernicus Sentinel-2 Satellite Data

Over the last two decades, Mozambique has experienced tremendous tropical cyclonic activities causing many flooding activities accompanied by disastrous human casualties. Studies that integrate remote sensing, elevation data and coupled with demographic analysis in Mozambique are very limited. This study seeks to fill the void by employing satellite data to map inundation caused by Tropical Cyclones in Mozambique. In pursuit of this objective, Sentinel-2 satellite data was obtained from the United States Geological Survey (USGS)’s Earth Explorer free Online Data Services imagery website covering the months of March 20, 2019, March 25, 2019, and April 16, 2019 for two cities, Maputo and Beira in Mozambique. The images were geometrically corrected to remove, haze, scan lines and speckles, and then referenced to Mozambique ground-based Geographic: Lat/Lon coordinate system and WGS 84 Datum. Data from twelve spectral bands of Sentinel-2 satellite, covering the visible and near infrared sections of the electromagnetic spectrum, were further used in the analysis. In addition, Normalized Difference Water Index (NDWI) within the study area was computed using the green and near infrared bands to highlight water bodies of Sentinel-2 detectors. To project and model the population of Mozambique and see the impact of cyclones on the country, demographic data covering 1980 to 2017 was obtained from the World Bank website. The Exponential Smoothing (ETS) method was adopted to forecast the population of Mozambique. Results from NDWI analysis showed that the NDWI is higher for flood areas and lower for non-flooded ones. The ETS algorithm results indicate that the population of Mozambique would nearly double by 2047. Human population along the coastal zone in the country is also on the rise exponentially. The paper concludes by outlining policy recommendations in the form of uniform distribution of economic activities across the country and prohibition of inland migration to the coastal areas where tropical cyclonic activities are very high.

Empirical Modeling of Annual Fishery Landings

Forecasting plays an essential role in policy formulation and implementation especially in the management of fisheries resources. In this paper, various techniques of forecasting using time series analysis were evaluated on annual fishery production data. In addition to the Box-Jenkins approach, other methods such as the feed forward neural network and exponential smoothing approaches were also examined. A parsimonious model for each forecasting approach was then selected using penalized likelihoods. The chosen models were then evaluated based on their ability to produce accurate forecasts. Implications of the findings as discussed revealed that no particular method was ideal for modeling all landings. Hence when forecasting fishery landings, it is recommended that different structural approaches be compared before selecting an appropriate one for use.

Charting the course:India’s health expenditure projections for 2035 Author links open overlay panel

Objectives Understanding past trends and forecasting future changes in health spending is vital for planning and reducing reliance on out-of-pocket(OOP)expenses.The current study analyzed health expenditure patterns in India and forecasted future trends and patterns until 2035.Methods Data on health expenditure in India from 2000 to 2019 was collected from the Organisation for Economic Co-operation and Development(OECD)iLibrary and National Health Accounts 2019 databases.Gross domestic product(GDP)data from the World Bank was also utilized.Descriptive statistics analyzed the composition and pattern,while the exponential smoothing model forecasted future health expenditures.Results The findings revealed that expenditure made by OOP is the primary health financing source,followed by government and pre-paid private spending.The percentage of GDP allocated to total health expenditure remains stable,while the per capita health expenditure fluctuates.Variations in expenditure among states are observed,with Karnataka relying heavily on pre-paid private coverage.Future projections suggest a decline in per capita and total health expenditure as a share of GDP,with a slight increase in the government’s share.Pre-paid private expenditure per capita and OOP health expenditure as a share of the total is projected to remain relatively constant but still high in absolute terms.Conclusion The study highlights variations in health spending in India,characterized by high OOP spending,limited public coverage,and a need for investments,and reforms to improve healthcare access and equity.

Real-time reliability prediction for dynamic systems with both deteriorating and unreliable components

As an important technology for predictive maintenance, failure prognosis has attracted more and more attentions in recent years. Real-time reliability prediction is one effective solution to failure prognosis. Considering a dynamic system that is composed of normal, deteriorating and unreliable components, this paper proposes an integrated approach to perform real-time reliability prediction for such a class of systems. For a deteriorating component, the degradation is modeled by a time-varying fault process which is a linear or approximately linear function of time. The behavior of an unreliable component is described by a random variable which has two possible values corresponding to the operating and malfunction conditions of this component. The whole proposed approach contains three algorithms. A modified interacting multiple model particle filter is adopted to estimate the dynamic system＇s state variables and the unmeasurable time-varying fault. An exponential smoothing algorithm named the Holt＇s method is used to predict the fault process. In the end, the system＇s reliability is predicted in real time by use of the Monte Carlo strategy. The proposed approach can effectively predict the impending failure of a dynamic system, which is verified by computer simulations based on a three-vessel water tank system.

Comparing practice-ready forecast models for weekly and monthly fluctuations of average daily traffic and enhancing accuracy by weighting methods

Knowing daily traffic for the current year is recognized as being essential in many fields of transport analysis and practice, and short-term forecasting models offer a set of tools to meet these needs. This paper examines and compares the accuracy of three representative parametric and non-parametric prediction models, selected by the analysis of the numerous methods proposed in the literature for their good combi- nation of forecast accuracy and ease of calibration, using real-life data on Italian motorway stretches. Non-parametric K-NN regression model, Gaussian maximum likelihood model and double seasonality Holt-Winters exponential smoothing model confirm their goodness to predict the weekly and monthly fluctuations of average daily traffic with varying degrees of performance, while maintaining an easy use in professional practice, i.e. requiring ordinary professional skills and conventional analysis tools. Since combining several prediction models can give, on average, more accuracy than that of the individual models, the paper compares two weighting methods of easy implementation and susceptible to a direct use, namely the widely used information entropy method and the less widespread Shapley value method. Despite being less common than the information entropy method, the Shapley value method proves to be more capable in better combining single forecasts and produces improvements in the predictions for test data. With these remarks, the paper might be of interest to traffic technicians or analysts, in various and not uncommon tasks they might find in their work.

Augmented Winter’s method for forecasting under asynchronous seasonalities

The method of Winters(1960)is one of the most well-known forecasting methodologies in practice.The main reason behind its popularity is that it is easy to implement and can give quite effective and efficient results for practice purposes.However,this method is not capable of capturing a pattern being emerged due to the simultaneous effects of two different asynchronous calendars,such as Gregorian and Hijri.We adapt this method in a way that it can deal with such patterns,and study its performance using a real dataset collected from a brewery factory in Turkey.With the same data set,we also provide a comparative performance analysis between our model and several forecasting models such as Winter’s(Winters 1960),TBAT(De Livera et al.2011),ETS(Hyndman et al.2002),and ARIMA(Hyndman and Khandakar 2008).The results we obtained reveal that better forecasts can be achieved using the new method when two asynchronous calendars exert their effects on the time-series.