Holt's Exponential Smoothing Research Articles

Rising COVID-19 Cases in Ghana and Bordering Neighbours. Correlating Impact on National Elections Backed with Holt’s Model

The COVID-19 pandemic poses an additional challenge to countries with planned national elections in 2020. In West Africa, moments of uncertainties engulf Ghana and her neighboring countries as they approach crucial stages of their democratic growth with impending presidential and legislative elections amidst rising COVID-19 cases. This paper identified the impact of COVID-19 on these critical national elections. COVID-19 daily case count and the accumulated infection, death, and recovery cases for each of the countries under study were assessed and Holt Exponential Smoothing (HES) forecasting method was adopted to predict the spread of the virus as the election cycle peaks in these countries. While the cumulative case count in Ghana is projected to hit 36,497 by December 23, case count in Ivory Coast and Burkina Faso will rise to 14086 and 2131, respectively after their elections. It was also established that the many activities that embody the electoral cycle could potentially exacerbate the cases in the respective countries which could affect voter participation and ultimately shape the dynamics of democracy and internal security. This study will serve as an important guide for improving decision-making and scenario planning for the elections in the sampled countries.

Forecasting End of COVID – 19 in India Based on Time Series Analysis

In this paper, the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models for active and exponential smoothing HOLT for removed rates has been estimated using daily time series data from 1st April to 14thSeptember 2020.The active and removed rates are computed from cumulative confirmed, active, recovered and deceased cases. It has been found that ARIMA (0, 1, 1) and Holt exponential smoothing Models are best fit for active and removed rates respectively. Normalized BIC is 0.577and 0.898 for active and removed rates respectively and is minimum among all the six models considered. Lack of fit of models is tested by Ljung-BoX Q statistic. The pvalue is 0.925 and 0.840 for active and removed rates respectively Since for both the rates p-value is greater than 0.05, hence conclude that our model does not show a lack of fit. On the basis of our analysis, active rate will be nullified latest by 5th January 2020, if everything goes best, as P M of India has assured on eve of Independence Day that vaccine for corona will be available very soon. Otherwise by 9 th February 2021 if the past trend continued and in worst situation it will tends to zero on 26th March 2021. We expect the removed rates will reach 100 percent by 20TH October 2020 if everything goes best and by 5th January 2021 if the past trend continued. On the assumptions that Pandemic will come to an end when removed rate in the population tends to 100 percent and active rate to zero percent. Thus on the basis our analysis we expect that COVID – 19 Pandemic may come to end latest either by 9 th February 2021 or 26th March 2021 subject to condition that the social distance and safely measures remains vigilance to stabilize and control the pandemic and in achieving India’s recovery from COVID-19.

Speech treatment in nemaline myopathy: A single-subject experimental study

PurposeThe objective of this work was to verify the efficacy of a treatment based on myofunctional therapy techniques which aimed to improve the tongue strength, precision, and speed of a ten-year-old girl with nemaline myopathy (NM) and the repercussions of this therapy on her speech intelligibility. NM is a rare congenital muscle disorder that causes extreme muscle weakness, especially in the face and neck, as well as severe dysarthria and dysphagia, although this does not affect the nervous system or cognitive development. MethodThis was a single-subject experimental study which used an interrupted pre- and post-treatment time-series design, and which applied autoregressive integrated moving-average predictive models and Holt exponential smoothing. During the treatment phases, the participant’s tongue strength and the rate of speech diadochokinesia and voluntary lingual movements were estimated and the repercussions of the intervention in terms of speech intelligibility were ascertained via an experiment with ‘naïve’ judges. ResultsThe treatment produced a sustained and significant increase in the maximum strength of the patient’s tongue, which increased from an initial 4 kPa to 11 kPa at the end of the treatment phase. However, this was far from the average 58 kPa for age- and sex-matched normative data. There were no significant changes either in the rates of voluntary lingual mobility or speech diadochokinesia. Speech intelligibility, as assessed by naïve judges, improved from 40 % in the pre-treatment phase to 67 % in the post-treatment phase. ConclusionsNM and other rare primary muscle disorders allow us to estimate the effects of severe muscle weakness in people with dysarthria without cognitive impairment or alterations in central nervous system, peripheral nervous system or in gap junction. In this case, the treatment did not increase the patient’s lingual and articulatory movement speed but did increase her tongue strength from 5 % to 10 % of the levels otherwise expected for her age and significantly improved the intelligibility of her speech and communication.

Amount of Poverty as Policy Basis: A Forecasting Using The Holt Method

This research aims to predict the growth of the number of poor people in every district and city in NTB Province of Indonesia for the next 10 years by using Holt exponential smoothing method. This type of research is quantitative research with input data used over the last 19 years with a measure of the goodness of models namely MSE, MAD, and MAPE. Based on the optimization results obtained the smallest parameter at α of 0.9 and β of 0.1, an average value MSE of 278005053.7, MAD of 9992.28222, and MAPE of 8.9374 Optimization results also provide information that the increase in the growth of the average poor population of 70206.6604 Certainly this result can be used as a guideline by the government in determining steps and responding to rapid response strategies in dealing with this poverty problem.

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.

Forecasting for a Data-Driven Policy Using Time Series Methods in Handling COVID-19 Pandemic in Jakarta

The coronavirus diseases 2019 or COVID-19 has spread and infected millions of people around the world. The on-going COVID-19 pandemic has taken an unprecedented toll on residents, business, commerce, and activity in many cities, including Jakarta, where there have been more than twelve thousand confirmed cases as of July 2020. The details of how COVID-19 spreads in Jakarta are still complicated and not completely understood because the number of infections is large and continues to climb. This paper conducts a quantitative analysis of the COVID-19 pandemic spreading using Jakarta as a case study for the evaluation and decision-making process. In this paper, time series models such as the Holt's exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) were used to forecast the number of COVID-19 cases in Jakarta between March 1 and July 6. Recently, data exploration and comparative analysis of time series models have been conducted to determine the optimal models for forecasting COVID-19 confirmed cases. The result shows that ARIMA has the highest R-Squared (R2), and lowest (Mean Squared Error) MSE and Root Mean Squared Error (RMSE) is the best model to forecast the upcoming number of infected cases of COVID-19 in Jakarta. Such a model shows promising results and fitting predictions in supporting data-driven policy in public health and epidemiology.

Prediction accuracy of sales surprise for inventory turnover

ABSTRACT Sales surprise, the ratio of actual sales to forecasted sales for a year, is a key determinant of firms’ inventory turnover. While the previous literature on empirical inventory management frequently uses Holt's double exponential smoothing method for obtaining future sales forecasts, this method suffers from measurement error. In contrast, we directly use the data on management forecasts publicised by listed companies in manufacturing and retail industries for the fiscal years 1997 through 2014, which are available in Japan, and evaluate the adequacy of the exponential smoothing sales forecasts. Our analysis reveals that sales surprise measured via both means positively relates to inventory turnover in the majority of industries, consistent with previous literature; however, the two variables imply discrepancies, particularly when economic conditions change.

90-90-90 cascade analysis on reported CLHIV infected by mother-to-child transmission in Guangxi, China: a modeling study

The prevalence of HIV in Guangxi was very high, and there were many children living with HIV (CLHIV) because of larger baseline of pregnant women infected by HIV. It is necessary for children to explore the status of antiretroviral therapy (ART) on different initial CD4 counts in children with HIV infected by mother-to-child transmission (MTCT) in Guangxi and to evaluate the progress towards the 90-90-90 targets proposed by UNAIDS/WHO. Based on a retrospective observational cohort of children with HIV infected from the Guangxi Center for Disease Prevention and Control (CDC), the variables of all patients included viral loads, CD4 counts, laboratory results and WHO clinical staging of HIV/AIDS were collected. Several indicators were defined before analyzed: (1) diagnosis of MTCT: infants born to HIV-positive mothers who tested positive for HIV twice before 18 months; (2) ART initiation: the children who were enrolled in the treatment cohort and were still having HIV monitoring as of 6 months before date censored and (3) viral suppression: a recently viral load measurement that was less than 1000 copies per milliliter. The number of CLHIV in Guangxi was projected by using the estimates of the national HIV/AIDS prevalence from China CDC. An Autoregressive Integrated Moving Average (ARIMA) model and the Holt Exponential Smoothing (ES) model were used to predict the number of CLHIV, the diagnosed CLHIV, the diagnosed CLHIV receiving ART and the number of them achieving viral suppression, in 2019 and 2021, respectively. In this 14-year HIV/AIDS treatment cohort, 807 children who were HIV infected by MTCT were enrolled. The ARIMA and Holt ES models showed that by the end of 2019, 82.71% of all CLHIV in Guangxi knew their HIV status, 84.50% of those diagnosed had initiated ART, and 85.68% of those on ART had durable viral suppression. By the end of 2021, 93.51% of all CLHIV in Guangxi will know their HIV status, 84.28% of those diagnosed will have initiated antiretroviral therapy, and 85.83% of those on ART will have durable viral suppression. Therefore, in 2021, Guangxi fails to achieve the WHO/UNAIDS 90-90-90 targets for CLHIV, and there is still a wide time interval between the first HIV-positive diagnosis and ART initiation. National free antiretroviral treatment program (NFATP) requires strong enforcement to reduce the prevalence of later chronic diseases and complications.

Forecasting of Onion Sown Area and Production in Turkey Using Exponential Smoothing Method

In 2017, 144 countries in the world produced 97.862.928 tons onion at 5.201.591 hectares. Turkey produced 2.1 million tons onion in 68 thousand hectares. Turkey was the seventh-largest producer country of dry onion with a share of 2,18% in the world. The main aim of this research was to forecast the onion area and production of Turkey for the period of 2019-2026. The data of this study was obtained from the database of the Food and Agriculture Organization and the time series covered the period of 1961-2018. Three Exponential Smoothing Methods were compared to model onion area and production and Holt Exponential Smoothing model was determined as the most appropriate forecasting model. In the study, time series data were determined as non-stationary and so, stationarity was obtained after taking the first difference of the time series. Model results show that, in the 2019-2026 period, the forecasted sown area of onion would be increased from 58.873 hectares to 60.981 hectares, forecasted production of onion would be increased from 2.066.453 tons to 2.309.751 tons. In order to reduce the effect of Cobweb theorem, onion production should be planned by producer organizations. The supply gap can be avoided by taking appropriate policy measures and this is necessary to maintain Turkey’s position in the world onion market.

TENDENCJE KONSUMPCJI WYROBÓW ALKOHOLOWYCH W EUROPIE W LATACH 2016-2019

W pracy przedstawiono wyniki badań nad wyznaczeniem tendencji konsumpcji czystego alkoholu na jedną osobę w latach 2017 – 2019 oraz analiza słuszności zastosowania podziału Europy na regiony zgodnie z klasyfikacją M49 według ONZ. Badanie zostało przeprowadzone dla krajów Europejskich, w podziale na Europę Północną, Europę Południową, Europę Zachodnią oraz Europę Wschodnia. W procesie prognozowania zastosowano metodę podwójnego wygładzania wykładniczego Holta. W wyniku przepro–wadzonych badań wykazano ogólne tendencje spadkowe zjawiska w omawianym okresie oraz możliwość zastosowania podziału terytorialnego według średnich wielkości spożycia całkowitego alkoholu w poszczególnych krajach europejskich.

A nonlinear grey forecasting model with double shape parameters and its application

The solution of Bernoulli differential equation can be described as a generalized Logistic curve function. Weibull cumulative distribution function is also an analytic solution of one variable coefficient differential equation. But, however because of the exact analytical solution problem of the equation, up to now, the developing coefficient of all the grey forecasting models is still defined as a constant. So, the aim of this paper is just to construct a novel grey differential equation model by combining NGBM(1,1) and Weibull cumulative distribution function. The proposed model(WBGM(1,1)) has the advantages of NGBM(1,1) and Weibull cumulative distribution. Where there are double shape parameters, the developing coefficient of the grey forecasting model is extended to be a variant. Property analysis of WBGM(1,1) shows that the fitting accuracy is higher and the applicable confines are wider. Finally, this paper gives an optimization method for the parameters of WBGM(1,1). A classic example and a practical case are studied for confirming the effectiveness of WBGM(1,1). The case study is the prediction for the number of invention patents of integrated circuit(IC) filed in China from 2007 to 2017. Results of the example and case study are compared to other forecasting models, including GM(1,1), NGBM(1,1), Holt exponential smoothing and ARIMA. Results show that WBGM(1,1) is a more general and more efficient model in grey prediction theory.

A retrospective and predictive study of fertility rates in China from 2003 to 2018

A retrospective longitudinal study was conducted on age-specific and birth-order-wise crude fertility rates and total fertility rates based on national sample surveys by the National Bureau of Statistics of China. Unexpectedly, such fertility datasets were officially deleted since 2016. A time-series predictive study was conducted based on the Holt's Exponential Smoothing models to restore the deleted fertility data for 2016 and beyond, allowing a comprehensive analysis of fertility rates in China from 2003 to 2018. In all, population structure was aging fast, fertility rates continued to decrease to a substantially low level, and three Northeastern provinces displayed notable socioeconomic issues associated with low-fertility trap. Adjustment is essential for China to timely remove its still-present birth limit and devise social policies to revert the fertility downtrend.

Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting

Wind-speed forecasting plays a crucial part in improving the operational efficiency of wind power generation. However, accurate forecasts are difficult owing to the uncertainty of the wind speed. Although numerous investigations of wind-speed forecasting have been performed, many of the previous studies used wind-speed data directly to make forecasts, which were rarely based on the structural characteristics of the data. Therefore, in this study, a hybrid linear-nonlinear modeling method based on the chaos theory was successfully employed to capture the linear and nonlinear factors hidden in chaotic time series. Before the forecast, the noise in the data was removed using a decomposition algorithm. Then, through the phase-space reconstruction, the one-dimensional time series were extended to the multi-dimensional space to determine the utilization form of the data. Finally, Holt's exponential smoothing based on the firefly optimization algorithm and support vector regression were combined to predict the wind speed. The experimental results show that the proposed model is not only better than the comparison models but also has great application potential in the wind power generation system.

For Whom the Bell Tolls: A Retrospective and Predictive Study of Fertility Rates in China

Background: Since 1980, China adopted a one-child policy for family planning. While the exclusive focus on total population control largely materialized, various negative effects occurred given the aging population and the fertility downtrend. After over 35 years, the one-child policy was terminated in 2016 to be replaced by a universal two-child policy. A comprehensive data-driven review of fertility rates in China was largely missing but urgently needed to diagnose the fertility reality. Methods: A retrospective longitudinal study was based on age-specific and birth-order-wise crude fertility rates (CFRs) from yearly national sample surveys by the National Bureau of Statistics (NBS) from 2003 to 2015. Summation of CFRs revealed total fertility rates (TFRs), a key parameter with reference replacement level of 2.1. Unexpectedly, fertility dataset was officially deleted in 2016 for unknown reasons. A time-series predictive study was conducted based on the Holt's Exponential Smoothing models to restore fertility data for 2016 and beyond. Using surveys from China Family Panel Studies, a third perspective of the completed fertility rates was also addressed. Finally, a comparative study among 31 administrative areas evaluated the association between fertility rates and key socioeconomic parameters (GDP growth, dependency ratio, social security etc.). Findings: Population structure was in the fast track of aging, and fertility rates continued to decrease to a very low level (TFR of 1.05 in 2015). Loosening birth limit had mild effect in raising 2nd births, but the catastrophic factor was the plummet in 1st births. Both the incoming shrinkage of fertile-age females and the transformative trend of delaying or even abandoning childbearing further added to a possible population crisis. Forecasts of fertility rates for 2016-2018 depicted continuity of the gloomy trend. TFR could be underestimated and would be lifted to around 1.5 after adjustment, but the pseudo completed fertility rates validated the still low fertility. Births produce not only humans but more importantly human resources. The three northeastern provinces in China with the lowest fertility were already suffering most socioeconomically. Interpretation: Past overoptimistic estimates or misjudgments delayed the essential adjustment to fertility policy. China should quickly remove the still-present birth limit and switch to encourage fertility. It is equally important to help young generation embrace 1st births, and eliminate the prerequisite of marriages for childbirth. The bell tolls for the boldest and most radical family planning policy to retire after four decades, before it's too late to make fertility replaceable again. Funding: Fundamental Research Funds for the Central Universities, the Ministry of Education, P.R. China. Declaration of Interest: The author declares no Conflict of Interest.

Análise de dados e previsão de séries temporais do homicídio doloso no Estado do Rio de Janeiro entre 2001 e 2016

A criminalidade vem sendo objeto de estudo há algum tempo por parte de pesquisadores de diversas áreas do conhecimento. O tema violência é sempre muito complexo, pois a sensação de insegurança abrange a sociedade como um todo e, diversas são suas causas e consequências. O objetivo desse artigo foi estudar o homicídio doloso no estado do Rio de Janeiro no período de 2001 a 2016. Foi apresentado o perfil das vítimas e, a análise e previsão da série temporal através dos modelos: Alisamento Exponencial Simples, Alisamento Exponencial Linear de Brown, Alisamento Exponencial de Holt e Alisamento Exponencial Sazonal de Holt-Winters. A eficácia dos modelos foi testada por duas medidas: o Erro Quadrático Médio e o Erro Percentual Médio Absoluto. Os resultados apontaram que o modelo de Alisamento Exponencial Sazonal de Holt-Winters obteve melhores resultados nas duas medidas.

Forecasting hotspots in East Kutai, Kutai Kartanegara, and West Kutai as early warning information

The aims of this research are to model hotspots and forecast hotspot 2017 in East Kutai, Kutai Kartanegara and West Kutai. The methods which used in this research were Holt exponential smoothing, Holt’s additive dump trend method, Holt-Winters’ additive method, additive decomposition method, multiplicative decomposition method, Loess decomposition method and Box-Jenkins method. For smoothing techniques, additive decomposition is better than Holt’s exponential smoothing. The hotspots model using Box-Jenkins method were Autoregressive Moving Average ARIMA(1,1,0), ARIMA(0,2,1), and ARIMA(0,1,0). Comparing the results from all methods which were used in this research, and based on Root of Mean Squared Error (RMSE), show that Loess decomposition method is the best times series model, because it has the least RMSE. Thus the Loess decomposition model used to forecast the number of hotspot. The forecasting result indicatethat hotspots pattern tend to increase at the end of 2017 in Kutai Kartanegara and West Kutai, but stationary in East Kutai.

An Automatic Forecasting Method for Time Series

An automatic forecasting method is proposed concerning automation problem in the field of linear time series forecasting. The method is on the basis of econometric theory and overcomes the difficulty to mine and forecast automatically with econometric models. The proposed algorithm is divided into 4 stages, i.e. preprocessing, unit root testing and stationary processing, modeling, and ultimately forecasting. Future values and trends would be estimated and forecasted precisely through the 4 stages of the algorithm according to input data without manual intervention. Experimental comparisons were made between the proposed algorithm and the 2 data driven forecasting algorithms, i.e. moving average method and Holt exponential smoothing method. It was demonstrated with the experimental results that automatic forecasting is feasible utilizing the proposed algorithm and higher accuracy can be acquired than these 2 data driven-based methods.

Determination of functional fitness age in women aged 50 and older

BackgroundThe construction of useful and attainable indicators of fitness assessment deserves special attention in clinical practice. We aimed to construct an indicator of the functional fitness age (FFA) of women aged 50 and older by an equation using fitness outcomes and its correlation with chronological age (CA) and to analyze the external validity of our results by comparing our sample to others. MethodsParticipants (n = 459, age: 70.3 ± 7.9 years, mean ± SD) were evaluated using the Senior Fitness Test battery. We applied a multiple regression and a subsequent Holt's exponential smoothing to analyze the outcomes. ResultsWe obtained a statistically significant expression of F(6, 452) = 328.384; p < 0.0005 in which the coefficients of the equation explain 81% of variability (R2corrected = 0.813). The equation correlates fitness assessment in women aged 50 and over with regards to CA: FFA = 40.146 + 0.350 × CS (stand) − 0.714 × AC (rep) − 0.110 × ST (step) − 0.177 × CSR (cm) − 0.101 × BS (cm) + 8.835 × FUG (s) where CS means chair stand test, AC means arm curl test, ST means 2-min step test, CSR means chair sit-and-reach test, BS means back scratch test, FUG means 8-foot up-and-go test. We compared this index with percentiles distribution from our sample and from other studies. ConclusionWe suggest the use of FFA as a valid indicator of fitness in adult and senior women as well as a useful motivational tool to undertake exercise programs.

Application of Holt exponential smoothing and ARIMA method for data population in West Java

One method of time series that is often used to predict data that contains trend is Holt. Holt method using different parameters used in the original data which aims to smooth the trend value. In addition to Holt, ARIMA method can be used on a wide variety of data including data pattern containing a pattern trend. Data actual of population from 1998-2015 contains the trends so can be solved by Holt and ARIMA method to obtain the prediction value of some periods. The best method is measured by looking at the smallest MAPE and MAE error. The result using Holt method is 47.205.749 populations in 2016, 47.535.324 populations in 2017, and 48.041.672 populations in 2018, with MAPE error is 0,469744 and MAE error is 189.731. While the result using ARIMA method is 46.964.682 populations in 2016, 47.342.189 in 2017, and 47.899.696 in 2018, with MAPE error is 0,4380 and MAE is 176.626.

Utilization of Holt&#039;s Forecasting Model for Zakat Collection in Indonesia

The practice of zakat is gaining popularity in Indonesia. This development is attributed to the strong role of the government in consistently developing zakat infrastructure and the increased awareness of people to practice zakat. Despite this success, a mechanism for predicting future zakat collection has not yet been developed. This study applies Holt's exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) model to forecast zakat in Indonesia using zakat collection from 2009 to 2014. Results show that Holt's exponential smoothing is best fits the zakat time series data and is therefore suitable for forecasting zakat. Holt's exponential smoothing is comparable to the ARIMA model given its small deviations in mean absolute percentage error and mean square error. Moreover, the software used to implement Holt's exponential smoothing is similar to that used in ARIMA models. These similarities show that these models can accurately forecast future trends to prepare proper strategies and plan the future of the organization. These models can also be used to develop a plan for managing charity based on the number of recorded mustahiq.