Double Exponential Smoothing Model Research Articles

Forecasting models analysis for predictive maintenance

IntroductionThis study explores the shift toward predictive maintenance through real-time data analytics to minimize machine downtime and improve machinery insights in industrial environments. Predictive maintenance aims to enable proactive interventions by predicting failures, enhancing operational efficiency.MethodsThe research was conducted in three stages. First, BA Glass equipment was sensorized using OPC Router and PowerStudio SCADA to facilitate real-time data extraction. A predictive maintenance algorithm was then developed in Python to analyze sensor data, predict failures, and trigger alarms. Finally, various forecasting models, including Linear and Polynomial Regression, Simple and Double Exponential Smoothing, ARIMA, and Prophet, were evaluated using a combination of blocked cross-validation and rolling window methodologies. The algorithm calculated performance metrics such as MSE, RMSE, and MAE for different parameter configurations and training sizes.ResultsA comparative analysis between wired and wireless sensors concluded that wireless sensors, although more expensive, were more practical and interchangeable in the factory setting. The results from the evaluation of prediction models showed that the Double Exponential Smoothing (DES) model with an additive damped trend and linear models performed best for datasets with daily seasonality and gradual oscillations. For datasets with stable trends and higher frequency oscillations, ARIMA and Prophet models proved to be more accurate.DiscussionThese findings suggest that the choice of sensors and prediction models can significantly impact the effectiveness of predictive maintenance systems. Wireless sensors offer long-term benefits in terms of flexibility and practicality, while the DES model and ARIMA/Prophet models are optimal depending on the dataset characteristics. This research highlights the value of real-time data analytics and predictive models in industrial environments for reducing downtime and improving decision-making.

Projection of HIV prevalence among individuals aged 15-49 years in Chile using Holt’s double exponential smoothing model

Projection of HIV prevalence among individuals aged 15-49 years in Chile using Holt’s double exponential smoothing model

Energy markets restructure beyond 2022 and its implications on Qatar LNG sales strategy: Business forecasting and trend analysis

The emergence of new suppliers and energy resources has reshaped the energy market in terms of contractual structures and pricing systems. The market shifts were accelerated in response to the latest Russian-Ukraine crisis, impacting natural gas supply chains from financing projects to contracting volumes. The increased demand for liquified natural gas volumes intensified the need to switch from long-term oil-indexed contracts to short-term gas-indexed contracts. Those shifts were anticipated to influence the selling strategies for the expected added 49 MTPA of Qatari LNG, wherein increasing the share of spot selling would be reflected in higher economic performance. This study used forecasted prices to investigate potential Qatari LNG selling strategies. Initially, projections of the most dominant pricing systems used for pricing Qatari LNG (i.e., brent, Henry Hub, Title Transfer Facility, and Japan Korea Marker) were estimated between 2023 and 2040. While Qatar has been relying on long-term oil-indexed contracts, the second step estimated annual LNG revenues under different combinations of selling strategies (i.e., long-term and spot sales). Finally, the influence of varying brent slopes on the estimated revenues was measured. Due to data limitations and non-stationarity, the double exponential smoothing model was selected among the different tested models. Considering current market dynamics, forecasts of the double exponential smoothing model showed an upward price trend until 2040. An annual average increase of 1.24% for the studied pricing systems was reported. Reducing the share of long-term brent-indexed contracts to 70% and dedicating the remaining 30% of volumes to spot sales yielded the highest premiums for revenue estimates. An average annual revenue of $62 bn was reported for the 70/30 strategy, around 6% higher than the 100% brent-indexed contracts strategy. The findings revealed that diversifying the selling approach and introducing spot sales can enhance revenues. From the buyers’ perspective, the outcomes support policymakers in understanding the implications of escalated prices driven by a lack of liquidity investments.

ESTIMATIONS OF GREEN HOUSE GASES EMISSIONS OF TURKEY BY STATISTICAL METHODS

The way of life, consumption habits, urbanization rate, type of energy production and increasing energy need with growing economies and population progressively promote the GHGs emissions to Earth’s atmosphere. GHGs consisting of CH4, N2O, CO2, H2O and HFCs cause the climate change, disrupting ecological balance, melting glaciers with global warming in the last decades. Therefore, the issues of future prediction and reduction of GHGs emissions became crucial for policy makers of Turkey and other countries under the international protocols and agreements. This article aims to present the prediction and 8-year future forecasting of CH4, N2O and CO2 emissions of Turkey using past annual data between years 1970 and 2018 with grey, autoregressive integrated moving average and double exponential smoothing models. Based on the results, the best prediction performance is reached by DES model followed by ARIMA and GM for all the emissions. MAPEs calculated from the available data and prediction by DES model from 1970 to 2018 are 0.285, 0.355 and 0.408 for CH4, N2O and CO2 in turn. DES future estimations of CH4, N2O and CO2 at 2026 year are determined as 50700 kiloton of CO2 eq., 38100 thousand metric ton of CO2 eq., and 512000 kilotons.

Forecasting a museum visit post pandemic using exponential smoothing model

This paper aims to evaluate the performance of a machine learning model for predicting the number of visitors to a museum after the COVID-19 pandemic. The easing of policies that began to be implemented by the Palembang city government after the end of the pandemic at the end of 2022 became a momentum in predicting the number of visits to the SMBII museum. During the pandemic the museum experienced a very drastic decline due to closures and restrictions on activities at the museum and had an impact on achieving the museum's targets in the fields of tourism and education. Museum managers need to establish a strategy as an effort to achieve the targets set during the post-pandemic period. This study predicts the number of visits to the SMBII museum in post-pandemic years by applying the double exponential smoothing (ESM) model. The dataset used is SMBII museum visit data which is divided into three categories of visitors, namely students, local and foreign. The evaluation results show that the double ESM model has the best performance with MSE = 3.8 and a = 0.9. The phenomena that occurred in the student visitor category affected ESM's performance in predicting visits where MSE in the post-pandemic period had a 200% higher value than before the pandemic which was influenced by the implementation of post-pandemic policies in museums. With the forecasting results in this study, it is hoped that it can become information in developing strategies and improving the performance of post-pandemic museums

Forecasting Performance of Double Exponential Smoothing Model and ETS Model for Predicting Crude Oil Prices

Purpose: This study aims to predict the price of monthly crude oil quickly and accurately by using an easy model and with easily available software.Design/methodology/approach: This study compares the DES-Holts and ETS models to predict price of monthly crude oil.Findings/result: The results of this study recommend the ETS(M,N,N) model to predict the price of monthly crude oil which produces an accuracy value of RMSE and MAPE of 4.385812 and 6.499007 %, respectively.Originality/value/state of the art: This study implements the DES_Holt's and ETS models to predict price of monthly crude oil with an RMSE and MAPE forecasting accuracy that has never been done in previous studies.

التنبُّؤ بسعر الصرف الفوري لليورو دولار في سوق (FOREX) استعمال المفاضلة بين طريقة هولت والشبكات العصبية الاصطناعية

In this paper , the time series data of the daily exchange rate of the euro against the dollar were used for the period from (12/7/2020 H:1 AM-10/12/2020 H:7 AM) with a total of (79) Observations as (120) Observations for comparison with the forecast values obtained using the two methods, to make the comparison process for future forecasts for the period from (10/12/2020 H:8 AM- 15/12/2020 H:7 AM) with a total of (120) Observations. To predict them, between the double exponential smoothing model (Holt method) and the feed-forward artificial neural network model using the two algorithms (Incremental Back Propagation algorithm, Quick Propagation algorithm).These methods are characterized by high accuracy and flexibility of these methods in the process of analyzing the time series, the results of the application showed that the most efficient and optimal model for representing the time series data is the artificial neural network model [2-10-1] using the Quick Propagation algorithm for the daily exchange rate of the euro against the dollar according to the criterion The mean square error ( MSE), has given lower indicators than the indicators of the artificial neural network model [2-10-1] using the Incremental Back Propagation algorithm, and the double exponential smoothing model (Holt method) when using (α =0.9 and = 0.1), which clearly indicates that it is the appropriate and efficient model for estimating future forecasts for the period from (10/12/2020 H:8 AM- 15/12/2020 H:7 AM). Where these values showed consistency with their counterparts in the original series, and provided us with a future picture of the reality of the daily exchange rate of the euro against the dollar for that period. Therefore, the artificial neural network model provided better future predictions than those provided by the double exponential smoothing model (Holt method), according to the standard of mean square error ( MSE), it gave less indicators than the indicators of the Holt model. The ready-made statistical programs MinitabV18 were used in the statistical side, and the ready-made neural network system program known as Alyuda NeuroIntelligence was used in the neural networks side.

Design of PM2.5 monitoring and forecasting system for opencast coal mine road based on internet of things and ARIMA Mode.

The dust produced by transportation roads is the primary source of PM2.5 pollution in opencast coal mines. However, China’s opencast coal mines lack an efficient and straightforward construction scheme of monitoring and management systems and a short-term prediction model to support dust control. In this study, by establishing a PM2.5 and other real-time environmental information to monitor, manage, visualize and predict the Internet of things monitoring and prediction system to solve these problems. This study solves these problems by establishing an Internet of things monitoring and prediction system, which can monitor PM2.5 and other real-time environmental information for monitoring, management, visualization, and prediction. We use Lua language to write interface protocol code in the APRUS adapter, which can simplify the construction of environmental monitoring system. The Internet of things platform has a custom visualization scheme, which is convenient for managers without programming experience to manage sensors and real-time data. We analyze real-time data using a time series model in Python, and RMSE and MAPE evaluate cross-validation results. The evaluation results show that the average RMSE of the ARIMA (4,1,0) and Double Exponential Smoothing models are 12.68 and 8.34, respectively. Both models have good generalization ability. The average MAPE of the fitting results are 10.5% and 1.7%, respectively, and the relative error is small. Because the ARIMA model has a more flexible prediction range and strong expansibility, and ARIMA model shows good adaptability in cross-validation, the ARIMA model is more suitable as the short-term prediction model of the prediction system. The prediction system can continuously predict PM2.5 dust through the ARIMA model. The monitoring and prediction system is very suitable for managers of opencast coal mines to prevent and control road dust.

PENERAPAN METODE EKSPONENTIAL SMOOTHING DALAM MEMPREDIKSI JUMLAH PESERTA DIDIK BARU DI SMA FAVORIT KOTA PAYAKUMBUH

Forecasting is a technique for estimating a value on a particular object in the future by paying attention to past data. This forecasting uses the Exponential Smoothing models because the data used is in accordance with the model. This study aims to predict the number of students in favorite high schools in Payakumbuh based on data obtained from 2014 until 2021 which is grouped into science and social studies classes. Forecasting is done using a Single Exponential Smoothing and Double Exponential models. MAPE results show that the Double Exponential Smoothing model is better at predicting the number of new students than Single Exponential Smoothing. Keywords: Double exponential smoothing, forecasting, single exponential smoothingMSC2020: 62M10

Wind speed prediction for small sample dataset using hybrid first‐order accumulated generating operation‐based double exponential smoothing model

AbstractWind power generation has recently emerged in many countries. Therefore, the availability of long‐term historical wind speed data at various potential wind farm sites is limited. In these situations, such forecasting models are needed that comprehensively address the uncertainty of raw data based on small sample size. In this study, a hybrid first‐order accumulated generating operation‐based double exponential smoothing (AGO‐HDES) model is proposed for very short‐term wind speed forecasts. Firstly, the problems of traditional Holt's double exponential smoothing model are highlighted considering the wind speed data of Palmerston North, New Zealand. Next, three improvements are suggested for the traditional model with a rolling window of six data points. A mixed initialization method is introduced to improve the model performance. Finally, the superiority of the novel model is discussed by comparing the accuracy of the AGO‐HDES model with other forecasting models. Results show that the AGO‐HDES model increased the performance of the traditional model by 10%. Also, the modified model performed 7% better than other considered models with three times faster computational time.

Forecasting Sugarcane Yield of India based on rough set combination approach

This study applied a novel rough set combination approach for forecasting sugarcane production in India. The paper uses autoregressive integrated moving average (ARIMA), double exponential smoothing (DES) and Grey model (GM) to generate the single forecasts. Also, the weight coefficient is evaluated by underlying the rough set approach to combine the single forecasts obtained from different models. To validate our proposed analysis, Sugarcane from 1950 to 2011 was used for the overall empirical analysis and generate out-sample forecasts from 2012 to 2021 for the comparative analysis. Also, ARIMA (2, 1, 1) model is found more appropriate for forecasting Sugarcane production.

Determining the Timing of Investment in Mutual Fund Products using the Double Exponential Smoothing Model

Purpose: As we know, COVID-19 affects the economy of most of the world, one of the investment media that continues to grow is mutual fund investment. This study aims to find the right time to invest by studying data in the previous period.
 Design/methodology/approach: In this study, we collect daily data on Net Asset Value (NAV) prices from January to September 2021, by analyzing calculations using the double exponential smoothing method to predict the time with the lowest price for investing.
 Findings: The results showed that this study was able to predict the right time to invest in mutual funds using the Holt Double Exponential Smoothing method using the actual smoothing data parameter of 0.9 (alpha) and the trend data smoothing parameter of 0.1 (beta) with produces a Mean Absolute Percentage Error (MAPE) value of 0.85343. The right time to invest in this research is determined on November 2, 2021 with an estimated net asset value (NAV) of 2348.644032.
 Paper type: Research paper
 Keywords: Mutual Fund, Investment Time, Investment Tool, Double Exponential Smoothing.

Trend Analysis and Short-Term Forecasting of Goat and Sheep Populations and their Meat Production in Sri Lanka using Single and Double Exponential Smoothing Models

Trend Analysis and Short-Term Forecasting of Goat and Sheep Populations and their Meat Production in Sri Lanka using Single and Double Exponential Smoothing Models

Disease burden of diabetes, diabetic retinopathy and their future projections in the UK: cross-sectional analyses of a primary care database

ObjectivesTo estimate the current disease burden, trends and future projections for diabetes mellitus (DM) and diabetic retinopathy (DR) in the IQVIA Medical Research Data (IMRD).Participants/design/settingWe performed a cross-sectional study of...

A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking.

This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate.

Climatic Temperature Data Forecasting in Nineveh Governorate Using the Recurrent Neutral Network Method

The forecasting of maximum climatic temperature is essential by using some statistical and intelligent techniques. Iraqi maximum temperature data collected monthly in several cities due to the Nineveh government will be studied in this paper. This study aims to forecast maximum climatic temperature as univariate time series and obtain the best results with minimum forecasting error. The non-linearity of climatic datasets is the main reason for data complexity, which needs to use some nonlinear methods for obtaining satisfactory results. In this paper, the maximum climatic temperature data will be forecasted by using traditional and intelligent methods. Single and double exponential smoothing (SES and DES) models have been used as traditional linear methods to forecast climatic temperature. The forecasting results reflected that the hybrid methods outperformed the traditional methods. The proposed hybrid methods can forecast climatic temperature in more accurate results. The hybrid methods SES-RNN and DES-RNN combine the SES and DES as a linear model with RNN as a nonlinear method to be one method that can handle any data, especially the nonlinear type. Recurrent neural network (RNN) as the nonlinear intelligent method is combined with SES and DES in hybrid SES-RNN and DES-RNN methods to forecast climatic temperature data and handle the non-linearity of datasets. The results reflect that the proposed hybrid methods outperformed the traditional methods for forecasting climatic temperature data. The proposed hybrid methods can be used to forecast climatic temperature in more accurate results.

Impact of level five lockdown on the incidence of COVID-19: lessons learned from South Africa.

Introductionthe level five (L5) lockdown was a very stringent social distancing measure taken to reduce the spread of COVID-19 infections. This study assessed the impact of the L5 lockdown and its association with the incidence of COVID-19 cases in South Africa (SA).Methodsdata was obtained from the National Department of Health (NDoH) from the 5th March to the 30th April 2020. A basic reproductive number (R0) and a serial interval were used to calculate estimated cases (EC). A double exponential smoothing model was used to forecast the number of cases during the L5 lockdown period. A Poisson regression model was fitted to describe the association between L5 lockdown status and incident cases.Resultsa total of 5,737 laboratory-confirmed cases (LCC) were reported by 30th April 2020, 4,785 (83%) occurred during L5 lockdown. Our model forecasted 30,629 cases of COVID-19 assuming L5 lockdown was not imposed. High incidence rates of COVID-19 were recorded in KwaZulu-Natal and Mpumalanga Provinces during the L5 lockdown compared to the other provinces. Nationally, the incident rate of COVID-19 was 68.00% higher in L5 lockdown than pre-lockdown for LCC.Conclusionthe L5 lockdown was very effective in reducing the incidence of COVID-19 cases. However, the incident rates of LCC and EC were higher nationally, and in some provinces during the L5 lockdown.

Prediction of CPI in Saudi Arabia: Holt’s Linear Trend Approach

The Consumer Price Index (CPI) indicates and measures price level changes in an economy based on total purchased goods and services. CPI is calculated by dividing the cost of the market basket in a given year by the cost of the market basket in a Base Year and multiplied by 100. CPI is subject to the Classification of Individual Consumption by Purpose (COICOP) in Saudi Arabia. Home expenditure items, such as housing, water, electricity, gas, and other fuels, food, and beverages are considered high while weight, health, education, recreation, and culture are given low weight in the COICOP in Saudi Arabia. Holt’s linear model based on three equations and popularly known as double exponential smoothing or linear exponential model and the most commonly used method in forecasting data based on internal trends. Holt’s linear model is composed of three equations relating to smoothing, trend, and forecast. In this paper, the CPI data were taken monthly from the General Authority for Statistics, Saudi Arabia for the period from May 2019 to July 2020 with 15 realizations. The CPI for Saudi Arabia is predicted for the next twelve months and as observed from the trend of CPI, the prices of total purchased goods will increase in the next eleven months.

Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model

The economic and health impacts resulting from the greenhouse effect is a major concern in many countries. The transportation sector is one of the major contributors to greenhouse gas (GHG) emissions worldwide. Almost 15 percent of the global GHG and over 20 percent of energy-related CO2 emissions are produced by the transportation sector. Quantifying GHG emissions from the road transport sector assists in assessing the existing vehicles’ energy consumptions and in proposing technological interventions for enhancing vehicle efficiency and reducing energy-supply greenhouse gas intensity. This paper aims to develop a model for the projection of GHG emissions from the road transport sector. We consider the Vehicle-Kilometre by Mode (VKM) to Number of Transportation Vehicles (NTV) ratio for the six different modes of transportation. These modes include motorcycles, passenger cars, tractors, single-unit trucks, buses and light trucks data from the North American Transportation Statistics (NATS) online database over a period of 22 years. We use multivariate regression and double exponential approaches to model the projection of GHG emissions. The results indicate that the VKM to NTV ratio for the different transportation modes has a significant effect on GHG emissions, with the coefficient of determination adjusted R2 and R2 values of 89.46% and 91.8%, respectively. This shows that VKM and NTV are the main factors influencing GHG emission growth. The developed model is used to examine various scenarios for introducing plug-in hybrid electric vehicles and battery electric vehicles in the future. If there will be a switch to battery electric vehicles, a 62.2 % reduction in CO2 emissions would occur. The results of this paper will be useful in developing appropriate planning, policies, and strategies to reduce GHG emissions from the road transport sector.

Forecasting Maternal Complications Based on the Impact of Gross National Income Using Various Models for Rwanda.

Introduction Preferably maternal mortalities are predominant in low- and middle-income countries (LMICs). In some African countries, including Rwanda, programs related to health-care delivery to reduce significantly severe complications including mortalities are established. Unfortunately, historical and forecasted maternal mortality reduction and the influence of gross national income (GNI) were not accessed. This study is aimed to forecast the three years of maternal mortalities (MMs) based on the influence of gross national income (GNI) in Rwanda. Methods The period involved is from January 2009 to April 2018. Data analyzed were obtained from the Central Hospital of the University of Kigali (CHUK) and mined data from the WHO database. Time series approach (Box-Jenkins and exponential smoothing) and linear regression models were applied. Besides, IBM-SPSS and Eviews were used in the analysis. Results The results revealed that MMs were not statistically different in several years, and there was a significant correlation between MMs and GNI (-0.610, P value 0.012 < 0.05). A double exponential smoothing model (DESM) was fitted for the best forecast and ARIMA (0,1,0) and linear regression models for a quick forecast. Conclusion There was a slight effect of GNI in maternal mortality reduction, which leads to the steady decrease of the forecasted maternal mortality up to May 2021. The Government of Rwanda should intensively strengthen the health-care system, save the children programs, and support pregnant women by using GNI for reducing MMs at an advanced level.