Holt-Winters Exponential Smoothing Research Articles

Impact of automotive tax incentives during Covid-19

In 2021, as part of the Pemulihan Ekonomi Nasional/ National Economic Recovery (PEN) program, the government introduced additional tax incentives to boost durable goods consumption, the PPnBM incentives, for qualified new car purchases. This study aims to explore the PPnBM incentives’ impact on automotive sectors in 2021, utilizing the Interrupted Time-Series Analysis (ITSA) methodology and seasonal forecasting method with Holt-Winters exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) for four years from 2018 until 2021. In particular, using the automobile wholesales and tax administration data, this paper assesses the impact of the PPnBM incentives on car wholesales, participating firms’ sales, purchases, and wage expenditure. The impact of the PPnBM incentives on automotive sectors in 2021 suggested no significant difference in incentives-qualifying cars’ wholesales before and after the PPnBM incentives. However, the impact of the PPnBM incentives on total sales recovery resulted in the incentive participants recovering their total sales to the pre-Covid-19 level. In general, from the variables tested, the result suggests that the PPnBM incentives had a modest impact on wholesale recovery and economic activity for participating automotive manufacturers during the incentives period.

The g-C3N4/rGO composite for high-performance supercapacitor: Synthesis, characterizations, and time series modeling and predictions

Supercapacitors have gathered significant interest in addressing the increasing need for energy storage devices with high power and energy densities. This study introduces a composite framework designed to enhance supercapacitor performance by leveraging the synergistic effects of g-C3N4/rGO composite. The composite electrode, which combines rGO for high conductivity and g-C3N4 for rapid ion diffusion, demonstrates excellent supercapacitor performance. The synthesized materials were characterized using different analytical tools such as X-ray diffraction, Fourier transform infrared spectroscopy, Brunauer-Emmett-Teller, Scanning and Transmission electron microscope, and X-ray photoelectron spectroscopy. The composite electrode exhibited outstanding performance characteristics, including a specific capacitance of 407.7 F/g, an energy density of 11.4 W h/kg, and a high power density of 186.7 W/kg. Kinetic analysis revealed that the g-C3N4/rGO composite electrode has a dominant diffusive controlled process. The g-C3N4/rGO electrode also demonstrated exceptional durability, maintaining remarkable capacitance retention even after the 5000th charge/discharge cycles. Moreover, the cyclic stability and Coulombic efficiency of the g-C3N4/rGO electrode were modeled and predicted by utilizing the time series analysis technique (Holt-Winters exponential smoothing). Considering the overall results, the g-C3N4/rGO composite electrode has a great potential for energy storage applications.

Prediction of acute onset of chronic cor pulmonale: comparative analysis of Holt-Winters exponential smoothing and ARIMA model

BackgroundThe aim of this study is to analyze the trend of acute onset of chronic cor pulmonale at Chenggong Hospital of Kunming Yan’an Hospital between January 2018 and December 2022.Additionally, the study will compare the application of the ARIMA model and Holt-Winters model in predicting the number of chronic cor pulmonale cases.MethodsThe data on chronic cor pulmonale cases from 2018 to 2022 were collected from the electronic medical records system of Chenggong Hospital of Kunming Yan’an Hospital. The ARIMA and Holt-Winters models were constructed using monthly case numbers from January 2018 to December 2022 as training data. The performance of the model was tested using the monthly number of cases from January 2023 to December 2023 as the test set.ResultsThe number of acute onset of chronic cor pulmonale in Chenggong Hospital of Kunming Yan’an Hospital exhibited a downward trend overall from 2018 to 2022. There were more cases in winter and spring, with peaks observed in November to December and January of the following year. The optimal ARIMA model was determined to be ARIMA (0,1,1) (0,1,1)12, while for the Holt-Winters model, the optimal choice was the Holt-Winters multiplicative model. It was found that the Holt-Winters multiplicative model yielded the lowest error.ConclusionThe Holt-Winters multiplicative model predicts better accuracy. The diagnosis of acute onset of chronic cor pulmonale is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors’ influence and try to incorporate them into the models.

Forecasting of Indian and foreign tourist arrivals to Himachal Pradesh using Decomposition, Box–Jenkins, and Holt–Winters exponential smoothing methods

Forecasting of Indian and foreign tourist arrivals to Himachal Pradesh using Decomposition, Box–Jenkins, and Holt–Winters exponential smoothing methods

Forecasting Rainfall in Planting Onion Crops in Brebes District, Brebes District Using Holt-Winters Exponential Smoothing

Forecasting Rainfall in Planting Onion Crops in Brebes District, Brebes District Using Holt-Winters Exponential Smoothing

COVID-19 Forecasting Using Recurrent Neural Network and Machine Learning

The COVID-19 variant’s complexity and the dire consequences of the variant spread are the inspirations behind the profound research on modeling and predicting new emerging variant surges. Multiple factors, including the variant characteristics, vaccination rate, the immune response of vaccinated individuals, and disease prevention health policies, impact the COVID-19 infection trend. The advancements in machine learning and neural network models, combined with the growth in computing, have demonstrated outstanding potential in modeling and predicting epidemic diseases. This research presents the modeling and prediction of the combined COVID-19 variant infection trends using the Holt-Winters exponential smoothing and seasonal auto-regressive integrated moving average with exogenous factors (SARIMAX) time-series machine learning models and recurrent neural network (RNN) long short-term memory (LSTM) model. Real-world United States COVID variant data from the Centers for Disease Control and Prevention (CDC) is used for prediction. The SARIMAX model factoring the seasonality of COVID-19 infections showed higher prediction accuracy than the Holt-Winters model, which is heavily weighted towards the most recent trends. The LSTM model had the best prediction accuracy of 91% with the lowest root mean square error (RMSE) values due to its property of selectively remembering patterns for long duration and the forget gates that correct the vanishing gradient problem, minimizing the error losses. This research demonstrates the promising application of the neural network deep learning models for epidemic disease modeling and prediction, enabling timely assessment of different policy decisions to mitigate the impact of an epidemic.

On Integrating Time-Series Modeling with Long Short-Term Memory and Bayesian Optimization: A Comparative Analysis for Photovoltaic Power Forecasting

The means of energy generation are rapidly progressing as production shifts from a centralized model to a fully decentralized one that relies on renewable energy sources. Energy generation is intermittent and difficult to control owing to the high variability in the weather parameters. Consequently, accurate forecasting has gained increased significance in ensuring a balance between energy supply and demand with maximum efficiency and sustainability. Despite numerous studies on this issue, large sample datasets and measurements of meteorological variables at plant sites are generally required to obtain a higher prediction accuracy. In practical applications, we often encounter the problem of insufficient sample data, which makes it challenging to accurately forecast energy production with limited data. The Holt–Winters exponential smoothing method is a statistical tool that is frequently employed to forecast periodic series, owing to its low demand for training data and high forecasting accuracy. However, this model has limitations, particularly when handling time-series analysis for long-horizon predictions. To overcome this shortcoming, this study proposes an integrated approach that combines the Holt–Winters exponential smoothing method with long short-term memory and Bayesian optimization to handle long-range dependencies. For illustrative purposes, this new method is applied to forecast rooftop photovoltaic production in a real-world case study, where it is assumed that measurements of meteorological variables (such as solar irradiance and temperature) at the plant site are not available. Through our analysis, we found that by utilizing these methods in combination, we can develop more accurate and reliable forecasting models that can inform decision-making and resource management in this field.

COVID-19 Case Growth Prediction Using a Hybrid Fuzzy Time Series Forecasting Model and a Machine Learning Approach

The COVID-19 pandemic has evolved into a global health crisis, with Indonesia particularly affected due to its high death rates compared to the rest of Asia. A significant number of unacknowledged, undocumented, or unaddressed cases further exacerbate the situation in Indonesia. Challenges arise from the growing patient population and a scarcity of resources, medical experts, and facilities. This study analyzes the daily development of COVID-19 cases in Indonesia, aiming to estimate the number of confirmed cases, recoveries, and fatalities. Introducing a novel hybrid forecasting model, we utilize the Holt-Winter triple exponential smoothing statistical method and the fuzzy time series rate of change algorithm. We apply the Triple Exponential Smoothing Holt Winter statistical model to predict future periods to the fuzzy time series. Based on the testing results, our proposed hybrid forecasting model demonstrates a very high level of predictive capacity. The acquired data are highly accurate, with a 0.15 percent confirmation rate, 0.15 percent recovery rate, and a 0.20 percent mortality rate, along with an average absolute error of less than 10% for each COVID-19 case. The findings indicate that early awareness by the COVID-19 Task Force of the status of cases is highly advantageous. This awareness can aid in formulating appropriate policies for future planning, organization, and accelerated treatment of COVID-19 in Indonesia. Consequently, successful efforts can be made to slow the emergence and spread of COVID-19 in the country.

Msf, a forecasting library to predict short-term electricity demand based on multiple seasonal time series

Transmission system operators have a growing need for more accurate forecasting of electricity demand. Current electricity systems largely require demand forecasting so that the electricity market establishes electricity prices as well as the programming of production units. The companies that are part of the electrical system use exclusive software to obtain predictions, based on the use of time series and prediction tools, whether statistical or artificial intelligence. However, the most common form of prediction is based on hybrid models that use both technologies. In any case, it is software with a complicated structure, with a large number of associated variables and that requires a high computational load to make predictions. On many occasions, the resources to reach these predictions are not available to researchers. In this paper we present a MATLAB® toolbox created to predict electrical demand. The toolbox implements multiple seasonal time series forecasting methods such as new generalized multiple seasonal Holt–Winters exponential smoothing models and neural network models, among others. The models presented include novel discrete interval mobile seasonalities (DIMS) to improve forecasting on special days. The paper also describes the computational analysis conducted to apply the toolbox in various electrical systems in Europe, where the results obtained can be seen. The use of this library opens a new way of research for the use of models with discrete and complex seasonalities in other fields of application.

Analysis and Forecasting of Consumer Price Index (CPI) in Kenya and South Africa using Holt Winter Model

In this paper, Holt-Winters exponential smoothing approach is applied to model and forecast monthly CPI in Kenya and South Africa. Monthly data from January 2000 to December 2023 was obtained from Central Bank of Kenya and South Africa department of statistics. Time series decomposition showed that the trend component is the most dominant component in both countries. Kenya Holt-Winters estimated model has parameters 0.6756, 0.0077 and 1 for level smoothing, trend smoothing and seasonal smoothing respectively. On the other hand, South Africa estimated model has parameters 0.8917, 0.1057 and 1 for level smoothing, trend smoothing and seasonal smoothing respectively. The estimated models are efficient and effective as on average the fitted values are less than one percent off the observed values. The initial values for level smoothing, trend smoothing and seasonal smoothing are approximately equal in both countries. The estimated models are then used to predict CPI next twelve months. Over the forecast period, South Africa will experience a lower index as compared to Kenya. In both countries, it’s expected that monthly CPI will rise.

FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING HOLT WINTER'S EXPONENTIAL SMOOTHING METHOD AND EXTREME LEARNING MACHINE METHOD

Airplanes provide comfort and speed for their users, especially for those who have limited time. The number of passengers has continued to increase in the last few months at Ahmad Yani International Airport, so a forecast is needed in making decisions to predict the number of passengers in order to maximize existing performance. The data used is secondary data on the number of airplane passengers at Ahmad Yani International Airport from 2012 to 2022 obtained from PT Angkasa Pura 1 (Persero). The Holt Winters Exponential Smoothing method is used because it aligns with the data pattern that includes trends and seasonality in the research, and it has a low level of accuracy. In this study also used the Extreme Learning Machine (ELM) method, apart from being a relatively new method, it has a fast learning speed and has low accuracy. This study aims to predict the number of airplane passengers at Ahmad Yani International Airport in Semarang using the Holt Winters Exponential Smoothing and ELM methods. The results of the analysis show that the MAPE value in the Holt Winters Exponential Smoothing method is 8,18% and in the ELM method using 12 input neurons and 43 neurons in the hidden layer, a MAPE of 6,04% is obtained. so that the ELM method is the right method for predicting the number of airplane passengers at Ahmad Yani International Airport in Semarang.

Forecasting Rice Prices with Holt-Winter Exponential Smoothing Model

Rice, as a staple food, plays a crucial role in global food security. Accurate forecasting of rice prices is essential for policymakers, farmers, and consumers alike. This article explores the application of the Holt-Winter exponential smoothing model to predict rice prices. Holt-Winter method is chosen for its ability to capture both trend and seasonality in time series data, which are prominent features in agricultural commodity prices such as rice. The study analyzes historical price data, identifies trends, seasonality, and incorporates smoothing parameters in additive and multiplicative methods. Results indicate that additive method of Holt-Winter exponential smoothing provides a better performance. This research contributes valuable insights to the field of agricultural economics and informs strategies for managing food supply chains and market stability.

Impact of COVID-19 pandemic in the Brazilian maternal mortality ratio: A comparative analysis of Neural Networks Autoregression, Holt-Winters exponential smoothing, and Autoregressive Integrated Moving Average models.

The acute respiratory infection caused by severe acute respiratory syndrome coronavirus disease (COVID-19) has resulted in increased mortality among pregnant, puerperal, and neonates. Brazil has the highest number of maternal deaths and a distressing fatality rate of 7.2%, more than double the country's current mortality rate of 2.8%. This study investigates the impact of the COVID-19 pandemic on the Brazilian Maternal Mortality Ratio (BMMR) and forecasts the BMMR up to 2025. To assess the impact of the COVID-19 pandemic on the BMMR, we employed Holt-Winters, Autoregressive Integrated Moving Average (ARIMA), and Neural Networks Autoregression (NNA). We utilized a retrospective time series spanning twenty-five years (1996-2021) to forecast the BMMR under both a COVID-19 pandemic scenario and a controlled COVID-19 scenario. Brazil consistently exhibited high maternal mortality values (mean BMMR [1996-2019] = 57.99 ±6.34/100,000 live births) according to World Health Organization criteria. The country experienced its highest mortality peak in the historical BMMR series in the second quarter of 2021 (197.75/100,000 live births), representing a more than 200% increase compared to the previous period. Holt-Winter and ARIMA models demonstrated better agreement with prediction results beyond the sample data, although NNA provided a better fit to previous data. Our study revealed an increase in BMMR and its temporal correlation with COVID-19 incidence. Additionally, it showed that Holt-Winter and ARIMA models can be employed for BMMR forecasting with lower errors. This information can assist governments and public health agencies in making timely and informed decisions.

Deep learning-based air pollution analysis on carbon monoxide in Taiwan

Global air pollution poses a threat to humanity. Specifically, CO directly affects cardiovascular and other organ tissues and leads to numerous chronic diseases and major public health problems. The effective implementation of a deep learning model for predicting variations in CO levels would enable the early formulation of policies for controlling air pollution. In this study, a seasonal gated recurrent unit (SGRU) model, which is a deep learning time-series prediction model, was developed to predict the levels of CO in Taiwan. Atmospheric CO measurements from 2005 to 2021 were collected from the Environmental Protection Administration of Taiwan and preprocessed using the Kalman filter to achieve accurate forecasting. The performance of the proposed SGRU model was compared with that of the autoregressive integrated moving average (ARIMA), seasonal ARIMA, exponential smoothing (ETS), Holt–Winters ETS, support vector regression, and seasonal long short-term memory models in terms of mean absolute percentage error (MAPE) and root mean square error. The SGRU model achieved the lowest MAPE value of 0.94, which demonstrated its superior performance. The construction of an accurate air pollution prediction model can assist government entities in formulating health and social care strategies and in planning future air pollution control measures.

Application of Holt-Winter Exponential Smoothing Method to Design a Drug Inventory Prediction Application in Private Health Units

This research aims to see how to design a drug supply prediction application using the Holt-Winter Exponential Smoothing method. The collection methods used in the research are as follows: observation, interviews. The prototyping system development method includes several important stages in designing research applications. The first stage is communication. After that, the Quick Plan stage. Construction of prototype stages. The final stage, deployment delivery and feedback, is the phase of system use by users. In addition, the team also conducts regular system maintenance to ensure optimal performance. These stages together form an iterative process for developing research applications using the prototyping method. Based on the research results, it was found that the test results for the drugs amoxicillin, cefixime, paracetamol, and CTM showed that the MAPE value was less than 10% for these four drugs, which means that the predictive value has quite high accuracy. So it can be concluded that the Holt Winter Exponential Smoothing algorithm can be well implemented to predict drug supplies.

Forecasting COVID-19 Daily Infected Cases in Sri Lanka by Holt-Winters Exponential Smoothing Method

The novel coronavirus disease (COVID-19) has spread from China since December 2019 and spread worldwide including Sri Lanka. The aim of this study was to forecast the daily infected cases of COVID-19 in Sri Lanka which in turn help administrators for effective management of the pandemic. The method used in this study was Holt-Winters three parameter with additive or multiplicative models. The daily infected cases in Sri Lanka during the period of 22nd January 2020 to 22nd December 2021 were obtained from the publicly available databases of Epidemiology Unit of Sri Lanka and World Health Organization. The pattern recognition of the daily infected cases was examined by time series plot and Auto Correlation Function (ACF). The model validation was performed by the Anderson Darling test which confirmed the normality of residuals (p > 0.05) and ACF that confirmed the independence of residuals of the model. The forecasting ability of the model was assessed by the three measurements of errors; Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Square Error (MSE). Holt-Winters additive and multiplicative model with α (level) 0.61, β (trend) 0.4 and γ (seasonal) 0.3 at a length of repeating behaviour of 3 days, had the least relative and absolute measurement of errors during the model fitting and verification. İn the multiplicative model, MAPE, MAD and MSE were 0.2847, 0.0187 and 0.0005 respectively. Similarly in the additive model, corresponding values of MAPE, MAD and MSE were 0.0207, 0.0187 and 0.0005. The fits and the forecast of these models followed a similar pattern of the actual daily infected cases concluding that the Holt-Winters model can be used to forecast the COVID-19 outbreak in Sri Lanka.

İnşaat Maliyet Endeksi Tahmininde Holt-Winters Üstel Düzeltme Parametrelerinin PSO ve İleri Walk-Forward Cross-Validation ile Optimizasyonu

This research aims to enhance the accuracy of Construction Cost Index (CCI) forecasting using Holt-Winters exponential smoothing (ES) by optimizing its parameters, focusing on minimizing the Mean Absolute Percentage Error (MAPE) for precise CCI forecasts. To reach this aim, The Holt-Winters model parameters are optimized through Particle Swarm Optimization (PSO) and Walk-Forward Cross-Validation (WFCV). PSO, a metaheuristic optimization algorithm, is being applied to search for optimal values of the smoothing parameters (alpha, beta, and gamma) that determine the weightage of past observations, trends, and seasonality, respectively. WFCV is assessed the model's performance and ensures robustness. Reduced MAPEs of 22 for CCI forecasts and 2 for training data are the findings of the optimized Holt-Winters model. The obtained alpha, beta, and gamma values are 0.99, 0.77, and 0, respectively, highlighting the importance of while neglecting seasonality. Convergence graphs demonstrate the superiority of the optimization approach over conventional parameter values or random selections. By employing PSO and WFCV, the study efficiently fine-tunes the Holt-Winters model for precise CCI forecasting. Optimized parameter values enable data driven decision-making in construction project cost estimation and budget management. This research contributes a reliable and robust optimization methodology for CCI forecasting, supporting advancements in the field.

Multidimensional analysis of losses in the number of passengers transported by air in Poland and Germany caused by the COVID-19 pandemic in terms of the maintenance of economic security

In the study, the authors conducted a multidimensional data analysis on the decrease in the number of passengers transported by air in Poland and Germany caused by the COVID-19 pandemic between 2020-2022. Forecasts of the number of passengers transported by air in Poland and Germany obtained after applying the Holt-Winters exponential smoothing method from January 2020 to December 2022 were compared with actual data. The research showed that in the case of Germany, in 2020 there was a decrease of 174 628 873 people, and in 2022, it was 90 620 354 people. In Poland, the decrease in 2020 amounted to 40 257 520 people; in 2022, it was already 21 083 066 passengers.

Analysis and Forecasting of Solar Power Generation using Machine Learning

Current photovoltaic systems are equipped with a monitoring system. The data is recorded with a predefined time base. The operator has a large amount of data. This article is a contribution to the analysis and understanding of this data. It proposes to estimate Generation from this data using machine learning. The study was conducted at the 1Mw generation site in Miroslava, Iasi, Romania. The visualization and interpretation of the generation data is presented in this article. Then, prediction techniques are presented to obtain an estimate of the plant’s generation. These techniques are: Simple Exponential Smoothing (SES), Autoregression (AR), Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMA), Holt Winter’s Exponential Smoothing (HWES), Long Short Term Memory neural (LSTM) and Convolutional Neural Network (CNN).

Analysis of road accident mortality based on time of occurrence for Kerala, India

Background: Road traffic accidents (RTA) pose a significant socio-economic burden and global public health concern. Monitoring road safety initiatives' efficacy necessitates analysing RTA incidence. This study examines time zone-specific RTA mortality in Kerala state, India, from 2016 to 2021. Methods: Utilizing compiled secondary-level time series data, the study encompasses total RTA fatalities in Kerala from 2016 to 2021. Data includes fatalities per year in nine consecutive three-hour time periods. Exploratory data analysis, time series regression, and exponential smoothing were employed for analysis. Results: Data reveals fluctuating trends in road accident (RA) fatalities, peaking in 2018 with a notable decrease in 2020. 18:00 to 21:00 recorded the highest and lowest fatalities, total 901 deaths. Disproportionate RA fatalities occurred from 06:00 to 09:00 (527 deaths) and 15:00 to 18:00 (697.5 deaths). The study employs Holt-Winters exponential smoothing for short-term forecasting, with a mean absolute scaled error (MASE) less than 1 signifying accurate predictions. Conclusions: The analysis highlights temporal patterns, emphasizing 18:00 to 21:00 as critical. Holt-Winters exponential smoothing proves vital for accurate short-term forecasting, with MASE reflecting precision. Urgency is stressed in adopting targeted measures for time-specific road accidents. Government intervention is pivotal, advocating for improved infrastructure, enhanced driver education, efficient vehicle management, and sustained traffic enforcement. Tailoring traffic laws to time zones, coupled with forecasting techniques, aligns with the overarching goal of enhancing road safety and reducing RA mortality rates.