Holt Linear Trend Research Articles

토픽모델링에 기반한 R&D 투자 포트폴리오 분석 및 예측

Many countries are identifying crucial technologies for national security and economic growth amid global tech competition, crafting cross-departmental strategies to pursue them. We propose a platform for analyzing and predicting R&D investment portfolios based on government R&D project information and investment data to provide scientific evidence for strategic budget allocation. The proposed platform consists of the following four processes. First, topic modeling and project labeling are performed using project-related text data. Second, we propose a method for calculating the importance of each label based on the centered log ratio (CLR) transformation. Third, we predict the label importance for the next year using linear regression and Holt's linear trend method. Fourth, we propose a method for constructing investment portfolio in specific fields. Real data analysis demonstrates the explanability and predictability of the proposed platform by using research project data spanning 11 years from 2012 to 2022. We believe a novelty of the proposed method can contribute to the establishment of data-driven R&D investment portfolios.

Utilization Trends of Phosphodiesterase Type-5 Inhibitors for Erectile Dysfunction Between 2019 and 2023 in Tanzania.

Introduction Erectile dysfunction (ED) profoundly affects millions of people globally, including interfering with mental health and quality of life. Phosphodiesterase type-5 inhibitors (PDE5Is) such as sildenafil are pivotal in ED treatment. This study aimed to examine the utilization patterns ofPDE5Is in Tanzania. Materials and methods In this retrospective longitudinal study, data on sildenafil and other similar PDE5Is imported between 2019 and 2023 were sourced from the Tanzania Medicines and Medical Devices Authority (TMDA). Pre-processing and visualization were performed using Microsoft Power BI Desktop, and further analysis was performed using IBM SPSS Statistics for Windows, Version 26 (Released 2019; IBM Corp., Armonk, New York, United States). Utilization trends were ascertained through curve fitting, Holt's linear trend model, and autoregressive integrated moving average (ARIMA) models. The defined daily doses (DDDS) per 1000 inhabitants (DID) were calculated using the World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) Classification System and the DDD methodology endorsed by the WHO Collaborating Centre for Drug Statistics Methodology. Results Between 2019 and 2023, there was a pronounced increase in the importation of approximately 587 consignments of PDE5Is. Employing the Holt model (R-square = 0.843), a substantial increase from 0.220910 DID in 2019 to 0.534272 DID by 2025 was observed and anticipated. The period witnessed sildenafil dominating 75.5% of the total use, with Erecto being the most consumed brand (37.6% of totalDID). Notably, 2022 had the highest surge (27.2% of the total), albeit a slight decline was observed in 2023 (20.5%). This trend was supported by a linear regression model (R-square = 0.889). Conclusion We found increasing annual trends of PDE5Is of utilization. This requires critical oversight and effective policies to ensure appropriate use and risk minimization.

Impact of Covid-19 Outbreak on Turkey Electricity Generation

With this study, it was aimed to analyze the change of electricity generation in Turkey depending on COVID-19 pandemic. For this purpose, analyzes of the changes in nine different electricity generation sectors besides total electricity generation were done. In addition, these analyze were also carried out on a monthly basis in order to better understand the change in total electricity generation. Trend, naive, exponential smoothing and holt linear trend methods were used for the analysis. The results were obtained by taking into account the 2020 forecast values of the method belonging to the mean absolute percentage error (MAPE) values obtained from these methods. According to these results, it was concluded that the COVID-19 epidemic negatively affected electricity generation from total, dam, lignite, stream, imported coal and wind and did not adversely affect electricity generation from natural gas, wind, solar, geothermal and biomass in Turkey for 2020. From the results of the analysis done for the monthly change in total electricity generation in 2020, it was seen that there was a decrease in the total generation compared to the expected in the months when there were restrictions.

A COVID-19 vaccine effectiveness model using the susceptible-exposed-infectious-recovered model

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused the start of the COVID-19 outbreak in the world, including Malaysia and Thailand. This study identifies the trend of the COVID-19 outbreak before and after the vaccination campaign by using the Susceptible-Exposed-Infectious-Recovered (SEIR) and Susceptible-Exposed-Infectious-Recovered-Vaccinated (SEIRV) models. Moreover, we predict the daily reported death and recovery cases using the SEIR model and Holt's linear trend method and then evaluate their performance. The data used in this study is real data from Malaysia and Thailand. The SEIRV model provides a comprehensive view of the efficacy of COVID-19 vaccinations in curbing the COVID-19 outbreak. This research reveals that the SEIR model outperforms Holt's linear trend method in predicting daily reported cases.

Poverty Level Forecasting in Maluku Province Using the Exponential Smoothing Method

The purpose of this study is to predict the poverty rate in Maluku Province in 2021-2025 using the Exponential Smoothing method. The Exponential Smoothing method is a moving average forecasting method that gives exponential or multilevel weight to the latest data so that the latest data will get greater weight. The data in this study were sourced from the Central Bureau of Statistics for Maluku Province. In this study, the results obtained were a comparison of several Exponential Smoothing methods, namely Simple Exponential Smoothing, Brown Linear Trend, and Holt Linear Trend. The best method for forecasting poverty levels in Maluku Province was obtained, namely the Holt Linear Trend Exponential Smoothing method with MAPE (3.251), RMSE (1.057), and MAE (0.722) values. Therefore, the poverty rate in Maluku Province is estimated at 18.14 in 2021, 18.29 in 2022, 18.44 in 2023, 18.59 in 2024, and 18.74 in 2025 with intervals between 4.46 and 33.02.

Modeling and forecasting of milk production in different breeds in Turkey

Identification of milk production is one of the key activities for the Turkish economy in terms of providing economic income for dairy smallholders and meeting consumer demands. This study aims to predict milk production in Turkey using various time series models which are BATS, TBATS, Holt's Linear Trend, ARIMA models, and NNAR. Yearly data from 1991 to 2019 on Milk Production is used in this study. The best time series model is selected from the testing data set (2015 to 2019) based on the lowest Mean Absolute Percentage Error (MAPE). The results of this study showed that the best predicts are obtained for Culture purebred milk production by an ARIMA (1,2,1) model, for Crossbreed milk production by a TBATS Model, for Indigenous milk production by ARIMA (0,2,0) model for total milking cows' production by Holt’s Linear Trend model. Furthermore, these models forecasted an increasing trend in the production of milk from 2020 to 2025. The percentage increases for culture purebred milk, Crossbreed milk production, and Indigenous milk production from 2020 to 2025 are projected to be 40, 20.9 and 10.9%, respectively. Overall, the total milking cows' production is projected to increase by 25% in 2025.

EFFICACY OF THE RICE CROP GROWTH USING DIFFERENT SMOOTHING METHODS

Rice is one of India's most important food crops. The government of India has a major duty in agriculture:perfect mapping and continual monitoring of paddy rice fields. Rice growth has a major impact on SCATSAT-1 backscatter images, and rice fields have been successfully mapped using a timeseries analysis employing satellite data. SCATSAT-1 time-series data was used to detect single-cropped, double-cropped, and triple-cropped rice fields (1 to 3 harvests per year) and identify different phonological stages using a crop phenology-based categorization. The usefulness of rice crop growth utilizing exponential smoothing approaches to estimate and forecast yield growth is demonstrated in this paper. The Holt linear trend, Holt-Winters methods (additive and multiplicative), and Mean Absolute Error (MAE), Sum Squared Error (SSE), Mean Squared Error (MSE), Mean Percentile Error (MPE), and Mean Absolute Percentage Error (MAPE) are used as error factors to choose the best forecasting methods among the exponential smoothing techniques.

Short-term Forecasting of Cumulative Confirmed Covid-19 Cases Pandemic in Somalia

Somalia has recorded the first confirmed Covid-19 case and first death case on March 16, and April 08, 2020, respectively. Since its arrival, it had infected 2,603 people and took the lives of 88 people while 577 patients were recovered as of 14 June, 2020. To fight this pandemic, the government requires to make the necessary plans accordingly. To plan effectively, the government needs to answer this question: what will be the effect of Covid-19 cases in the country? To answer this question accurately and objectively, forecasting the spread of confirmed Covid-19 cases will be vital. To this regard, this paper provides real times forecasts of Covid-19 cases employing Holt's linear trend model without seasonality. Provided that the data employed is accurate and the past pattern of the disease will continue in the future, this model is powerful to produce real time forecasts in the future with some degree of uncertainty. With the help of these forecasts, the government can make evidence based decisions by utilizing the scarce resource available at its disposal.

Machine learning and optimization models for supplier selection and order allocation planning

Supplier selection and order allocation have significant roles in supply chain management. These processes become major challenges when the demand is uncertain. This research presents a new two-stage solution approach for supplier selection and order allocation planning where a forecasting procedure is integrated with an optimization model. In the first stage, the demand is forecasted to handle the demand vagueness. A novel Relational Regressor Chain method is introduced to determine the future demand, which is compared with the Holt's Linear Trend and the Auto-Regressive Integrated Moving Average methods to ensure the forecasting accuracy. The forecasted demand is then fed to the second stage where a multi-objective programming model is developed to identify suitable suppliers and order quantities from each supplier. Weighted-sum and ε-constraint methods are utilized to obtain the efficient solutions. To our knowledge, this paper is the first study that has integrated demand forecasting with the supplier selection and order allocation planning. A real dataset from a Canadian food supply network is used to examine the results of the forecasting methods and to determine the orders allocated to each supplier. The results of the forecasting methods show that the proposed Relational Regressor Chain method can forecast demand with a higher precision than the other forecasting methods considered in this paper. It is also evident from the results that the selection of the forecasting methods may have impact on both the selection of suppliers and the orders allocated to them.

Development of Algorithms for Choosing the Best Time Series Models and Neural Networks to Predict COVID-19 Cases

Time series analysis became one of the most investigated fields of knowledge during spreading of the COVID-19 around the world. The problem of modeling and forecasting infection cases of COVID-19, deaths, recoveries and other parameters is still urgent. Purpose of the study. Our article is devoted to investigation of classical statistical and neural network models that can be used for forecasting COVID-19 cases. Materials and methods. We discuss neural network model NNAR, compare it with linear and nonlinear models (BATS, TBATS, Holt's linear trend, ARIMA, classical epidemiological SIR model). In our article we discuss the Epemedic.Network algorithm using the R programming language. This algorithm takes the time series as input data and chooses the best model from SIR, statistical models and neural network model. The model selection criterion is the MAPE error. We consider the implementation of our algorithm for analysis of time series for COVID -19 spreading in Chelyabinsk region, and predicting the possible peak of the third wave using three possible scenarios. We mention that the considered algorithm can work for any time se-ries, not only for epidemiological ones. Results. The developed algorithm helped to identify the pat-tern of COVID -19 infection for Chelyabinsk region using the models realized as parts of the consi-dered algorithm. It should be noted that the considered models make it possible to form short-term forecasts with sufficient accuracy. We show that the increase in the number of neurons led to in-creasing accuracy, as there are other cases where the error is reduced in case of reducing the number of neurons, and this depends on COVID -19 infection spreading pattern. Conclusion. Hence, to get a very accurate forecast, we recommend re-running the algorithm weekly. For medium-range fore-casting, only the NNAR model can be used from among those considered but it also allows to get good forecasts only with horizon 1–2 weeks.

Modelling and forecasting of web traffic using Holt's linear, bats and TBATS models

In the recent era, internet consumption has increased. Due to this heavy and regular use of internet web traffic is increased. Sometimes due to high web traffic server may also affect. In this study, cumulative data on the number of visitors to Wikipedia, Facebook, Energy, Android, and Apple, are analyzed in detail. Some descriptive statistics of visitor’s pages in Wikipedia are given such as mean, minimum, maximum, standard deviation, skewness and kurtosis. The present study used different time series models like Holt’s Linear Trend, BATS and TBATS for different web pages. From the results, it was found that the Android page as well as apple page in Wikipedia holt’s Linear Trend model performance is better compared to other models. This kind of projection is helpful for web traffic to solve the server breakdown problem for larger users of the server. These Wikipedia pages have been chosen to Forecast the number of visitors to these pages through time series models BATS, TBATS, and Holt's Linear Trend Model in order to face future problems to mitigate over loading that may occur with the increase in the number of visitors to these pages and also to experiment the suitability of these models of Time series to Forecast the number of visitors and that to achieve the highest level of accuracy. MAPE for accuracy was used to compare model performance.

A model of predicting performance of Olympic female weightlifters using time series analysis

The purpose of this study was to predict the performance of female weightlifters using time series analysis. Based on this purpose, a time series analysis was used to calculate the performance prediction model for women(58kg) among the domestic women weightlifters who participated in the Olympics. As a result of creating time series data based on 10 years of record and then evaluating the sequential charts of each athlete group, the female athletes' records did not show any seasonality or difference. In addition, after examining the independence of the data through the creation of a time series model, it was shown that the models produced conformed to the criteria for compliance and that there was no difference in the data, but there was a trend. Accordingly, Holt linear trend analysis of the exponential smoothing model was applied. As a result of deriving the prediction model of the athletes through this process, it was found that the women (58kg) who participated in the Olympics continued to improve within the range of 166.11kg to 184.1kg.

Application of machine learning time series analysis for prediction COVID-19 pandemic

PurposeCoronavirus disease is an irresistible infection caused by the respiratory disease coronavirus 2 (SARS-CoV-2). It was first found in Wuhan, China, in December 2019, and has since spread universally, causing a constant pandemic. On June 3, 2020, 6.37 million cases were found in 188 countries and regions. During pandemic prevention, this can minimize the impact of the disease on individuals and groups. A study was carried out on coronavirus to observe the number of cases, deaths, and recovery cases worldwide within a specific time period of 5 months. Based on this data, this research paper will predict the future spread of this infectious disease in human society.MethodsIn our study, the dataset was taken from WHO “Data WHO Coronavirus Covid-19 cases and deaths-WHO-COVID-19-global-data”. This dataset contains information about the observation date, provenance/state, country/region, and latest updates. In this article, we implemented several forecasting techniques: naive method, simple average, moving average, single exponential smoothing, Holt linear trend method, Holt-Winters method and ARIMA, for comparison, and how these methods improve the Root mean square error score.ResultsThe naive method is best suited as described over all other methods. In the ARIMA model, utilizing grid search, we recognized a lot of boundaries that delivered the best-fit model for our time series data. By continuing the model, future predictions of death cases indicate that the number of deaths will increased by more than 600,000 by January 2021.ConclusionThis survey will support the government and experts in making arrangements for what is about to happen. Based on the findings of instantaneous model, these models can be adjusted to guide long time.

Modeling of COVID-19 Outbreak Indicators in China Between January and June.

The objective of this study is to compare the various nonlinear and time series models in describing the course of the coronavirus disease 2019 (COVID-19) outbreak in China. To this aim, we focus on 2 indicators: the number of total cases diagnosed with the disease, and the death toll. The data used for this study are based on the reports of China between January 22 and June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model. Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths. Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85,589 and 4639, respectively. The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations.

Application of the exponential smoothing model and ARIMA model in prediction of the endemic situation of schistosomiasis in Hunan Province

To predict the changes in the prevalence of Schistosoma japonicum infections in humans and livestock in Hunan Province using the exponential smoothing model and the ARIMA model. The data pertaining to S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015 were collected, and the exponential smoothing model and the ARIMA model were created using the software Eviews and PASW Statistics 18.0. In addition, the effectiveness of these two models for the prediction of S. japonicum infections in humans and livestock in Hunan Province from 2016 to 2018 was evaluated. The exponential smoothing model and the ARIMA model had a high goodness of fit for prediction of S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015. There was a linear trend in the prevalence of S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015. The prevalence of S. japonicum infections in humans predicted with the Brown's linear trend and the prevalence of S. japonicum infections in livestock predicted with the Holt's linear trend in Hunan Province from 2016 to 2018 fitted better the actual data than the ARIMA model; however, prediction of the ARIMA model indicated that the endemic situation of schistosomiasis remained at a low level in Hunan Province. At a low epidemic level, development of highly sensitive tools for monitoring schistosomiasis is urgently needed in Hunan Province to fit the current endemic situation, and the schistosomiasis control measures should be intensified to consolidate the control achievements.

Forecasting Electricity Consumption Using Time Series Model

Electricity demand forecasting is important for planning and facility expansion in the electricity sector. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development. Universiti Tun Hussein Onn Malaysia (UTHM) which is a developing university in Malaysia has been growing since its formation in 1993. Thus, it is important for UTHM to forecast the electricity consumption in future so that the future development can be determined. Hence, UTHM electricity consumption was forecasted by using the simple moving average (SMA), weighted moving average (WMA), simple exponential smoothing (SES), Holt linear trend (HL), Holt-Winters (HW) and centered moving average (CMA). The monthly electricity consumption from January 2011 to December 2017 was used to forecast January to December 2018 monthly electricity consumption. HW gives the smallest mean absolute error (MAE) and mean absolute percentage error (MAPE), while CMA produces the lowest mean square error (MSE) and root mean square error (RMSE). As there is a decreasing population of UTHM after the moving of four faculties to Pagoh and HW forecasted trend is decreasing whereas CMA is increasing, hence HW might forecast better in this problem.

ModisCfied Holt s Linear Trend Method

Exponential smoothing models are simple, accurate and robust forecasting models and because of these they are widely applied in the literature. Holt's linear trend method is a valuable extension of exponential smoothing that helps deal with trending data. In this study we propose a modified version of Holt's linear trend method that eliminates the initialization issue faced when fitting the original model and simplies the optimization process. The proposed method is compared empirically with the most popular forecasting algorithms based on exponential smoothing and Box-Jenkins ARIMA with respect to its predictive performance on the M3-Competition data set and is shown to outperform its competitors.

Product Demand Forecasting with a Novel Fuzzy CMAC

Forecasting product demand has always been a crucial challenge for managers as they play an important role in making many business critical decisions such as production and inventory planning. These decisions are instrumental in meeting customer demand and ensuring the survival of the organization. This paper introduces a novel Fuzzy Cerebellar-Model-Articulation-Controller (FCMAC) with a Truth Value Restriction (TVR) inference scheme for time-series forecasting and investigates its performance in comparison to established techniques such as the Single Exponential Smoothing, Holt's Linear Trend, Holt-Winter's Additive methods, the Box-Jenkin's ARIMA model, radial basis function networks, and multi-layer perceptrons. Our experiments are conducted on the product demand data from the M3 Competition and the US Census Bureau. The results reveal that the FCMAC model yields lower errors for these data sets. The conditions under which the FCMAC model emerged significantly superior are discussed.

Kentucky property insurance surtax revenue: a comparison of alternative forecasting models

Given the absence of a formal forecasting model of property insurance surtax revenue for the state of Kentucky, this paper presents the insample and out-of-sample forecasts of four models: Holt linear trend algorithm, autoregressive model, linear trend/autoregressive model, and economic activity model based on annual fiscal year data from 1984 to 2001. The Holt linear trend algorithm and the linear trend/autoregressive model were reasonably close in their respective forecasting performance for both the in-sample and out-ofsample forecast horizons. However, the linear trend/autoregressive model exhibited some evidence of instability for the period 1992 to 1994. With respect to the out-of-sample forecasts, the Holt linear trend algorithm provided a better fit to the actual surtax data. Moreover, as time passes and additional data on the surtax becomes available, the models presented can easily be updated and reevaluated.