Exponential Smoothing Model Research Articles

Charting the course: India’s health expenditure projections for 2035

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

A Study of China's Electricity Supply Based on Correlation and Time Series Modeling

In this study, the relationship between electricity supply and various indicators is deeply explored through multi-factor correlation analysis, quantitative analysis and prediction modeling. First, the Pearson correlation coefficient analysis was used, and it was revealed that electricity supply is significantly and positively correlated with gross domestic product (GDP), per capita GDP, urban population ratio, and consumption level of residents. Subsequently, linear regression methods were used for quantitative analysis, and forecasting models for electricity supply were established, including ARIMA model and Holt-Winters seasonal exponential smoothing model. Finally, the forecast value of China's electricity supply for the next 37 years was derived by synthesizing the forecasting models. The results of this study provide an important reference for optimizing the structure of electric power supply and improving the supply efficiency, and are of guiding significance for the formulation of relevant policies and planning.

Predicting of aging population density by a hybrid grey exponential smoothing model (HGESM): a case study from Sri Lanka

PurposeThe global population has been experiencing an unprecedentedly rapid demographic transition as the populations have been growing older in many countries during the current decades. The purpose of this study is to introduce a Grey Exponential Smoothing model (GESM)-based mechanism for analyzing population aging.Design/methodology/approachTo analyze the aging population of Sri Lanka, initially, three major indicators were considered, i.e. total population, aged population and proportion of the aged population to reflect the aging status of a country. Based on the latest development of computational intelligence with Grey techniques, this study aims to develop a new analytical model for the analysis of the challenge of disabled and frail older people in an aging society.FindingsThe results suggested that a well-defined exponential trend has been seen for the population ages 65 and above, a total of a million) during 1960–2022; especially, the aging population ages 65 and above has been rising rapidly since 2008. This will increase to 24.8% in 2040 and represents the third highest percentage of elderly citizens living in an Asian country. By 2041, one in every four Sri Lankans is expected to be elderly.Originality/valueThe study proposed a GESM-based mechanism for analyzing the population aging in Sri Lanka based on the data from 1960 to 2022 and forecast the aging demands in the next five years from 2024 to 2028.

Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble

AbstractBase metals are key materials for various industrial sectors such as electronics, construction, manufacturing, etc. Their selling price is important both for the profitability of the mining and metallurgical companies that produce and trade them, as well as for the countries whose economies rely on their exports or tax revenues as a means for national income. Prices are also critical for companies that use base metals as inputs to fabricate end products. The prediction of prices’ future movements can serve as a tool for risk mitigation and better budget planning. In this study, the logarithmic returns of base metals are forecasted using an autoregressive Light Gradient Boosting Machine (LightGBM) as well as an ensemble comprising the aforementioned algorithm and a classical time series forecasting model (i.e., ARIMA). The two models are then compared to three simpler benchmark models, namely a global mean model, an exponential smoothing model and an ARIMA model. When comparing using RMSE, the autoregressive LightGBM model outperformed the three univariate benchmark models (and the ensemble) for forecasting 6 months ahead for aluminum and nickel returns, while copper and zinc returns were forecasted better by the ensemble. Neither of the proposed models performed better than an ARIMA model when it comes to forecasting lead and tin returns.

Research on the income and expenditure forecast of long-term care insurance fund in Shihezi City based on community care.

This study forecasts the income and expenditures of the long-term care insurance fund, provides a basis for formulating the raising standard of the long-term care insurance fund, and explores the measures to improve the pilot work of long-term care insurance. By using the exponential smoothing and ARIMA models to forecast the income and expenditure of the old-age care insurance fund in 2022, the problems existing in the operation of the long-term care insurance fund are discussed. In 2022, the income of the old-age insurance fund was 28.8934 million yuan, and the fund compensation expenditure was 28.4070 million yuan, with a slight balance of the fund. The highest relative errors of income and expenditure forecast models are -2.03% and - 2.76%, respectively. According to the results of fund expenditure, the annual financing standard should be 132.93 yuan/person, and the individual financing standard should be 66.47 yuan/person. Through the integration of personal payment, welfare, sports lottery public welfare income, social donations, and other ways, we can gradually establish a multi-channel risk-sharing financing. We will appropriately raise the standard for individual financing and the annual contribution standard for individuals from 50 yuan to 66.47 yuan. This will promote sustainable development of long-term insurance system.

Precision in Insurance Forecasting: Enhancing Potential with Ensemble and Combination Models based on the Adaptive Neuro-Fuzzy Inference System in the Egyptian Insurance Industry

ABSTRACT Enhancing the precision of retention ratio predictions holds profound significance for insurance industry decision-makers and those vested in advancing insurance services. Precision helps insurance companies navigate inflationary pressures and evaluate underwriting profitability, enabling reliable prognoses of future underwriting gains. As far as we know, although there have been multiple attempts to construct a predictive model for retention ratio, none of these attempts have used combining models or studied the Egyptian market. Therefore, this study contributes significantly to this developing field by providing combining models, which combined statistical time series models such as Exponential Smoothing (ES), and Autoregressive Integrated Moving Average (ARIMA), with Adaptive Neuro-Fuzzy Inference System (ANFIS). Two different types of combinations are employed with these models. Furthermore, the study introduces three ensemble models designed for the purpose of predicting the retention ratio within the Egyptian insurance market. Dataset was carefully gathered from the EFSA’s annual reports, focused on the property-liability insurance sector within the Egyptian insurance market and covers the time period from 1989 to 2021. Next, the proposed models are assessed employing well-established statistical assessment metrics, namely, Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), R Square (R2), and Root Mean Square Error (RMSE). The results show that combining and ensemble methods improve predicted accuracy. A multi-linear regression-based ensemble model that combines ARIMA, ES, and ANFIS models outperforms both single and combined models in robustness. The article concludes that the insurance industry can greatly benefit from modern predictive methods to make sound decisions.

Forecasting the Turkish lira Exchange Rates Through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?

Forecasting the Turkish lira Exchange Rates Through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?

The Forecast of Streamflow through Göksu Stream Using Machine Learning and Statistical Methods

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system.

The Role of Smart City in Achieving Sustainable Development: Google Trends Analysis and Exponential Time Series Smoothing Models

The concept of sustainable smart urban development is an important element of the modern discourse on the future development of cities, regions, and the world as a whole, a key component of a strategy aimed at introducing innovations, improving the life quality of citizens, ensuring inclusiveness and sustainability of local development. It integrates the concept of Smart City as the application of advanced innovative technologies to solve urban problems and the concept of Sustainable Development as a balancing of socio-economic and environmental interests to achieve the Sustainable Development Goals. While discussing the problems and prospects introducing any social models or strategies, it is important to understand the level of public interest in this issue, the level of citizens’ awareness, the readiness to receive new information, and the readiness for change. The article is focused on the analysis of retrospective dynamics (from 01.01.2010 to 01.12.2023) and economic and mathematical modelling of forecast trends in changing public interest in the concepts of “Smart City” and “Sustainable Development” (identified using the Google Trends tool). The analysis was carried out for the world as a whole and for Ukraine specifically. The study identifies and describes the evolution of changes in public interest in these topics, identifies the time points of a sharp surge in interest, and conducts a comparative analysis in the content (trends for these two search queries) and spatial context (trends in Ukraine compared to global trends). The countries of the world and regions of Ukraine where user interest in the relevant topics dominates are ranked, and the countries of the world and regions of Ukraine where user interest in the relevant topics dominates are ranked, the TOP-10 are identified. The factors whose influence is relevant to the public interest dynamics are identified (consumer trends, media activity, dynamics of the introduction of relevant concepts, cultural, linguistic, and historical contexts, the general level of education, digital inclusion and consciousness of the population of different countries, etc.) Using the exponential smoothing method, models were built to predict the dynamics of time series – the number of queries from Ukrainian Internet users regarding the concepts of “Smart City” and “Sustainable Development”. Additive forecasting models, which differ in the trend component, are obtained.

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

This study addresses the critical challenge of accurately forecasting electricity consumption by utilizing Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The research aims to enhance the precision of forecasting in the dynamic energy landscape and reveals promising outcomes by employing a robust methodology involving model application to a large amount of consumption data. Exponential Smoothing demonstrates accurate predictions, as evidenced by a low Sum of Squared Errors (SSE) of 0.469. SARIMA, with its seasonal ARIMA structure, outperforms Exponential Smoothing, achieving lower Mean Absolute Percentage Error (MAPE) values on both training (2.21%) and test (2.44%) datasets. This study recommends the adoption of SARIMA models, supported by lower MAPE values, to influence technology adoption and future-proof decision-making. This study highlights the societal implications of informed energy planning, including enhanced sustainability, cost savings, and improved resource allocation for communities and industries. The synthesis of model analysis, technological integration, and consumer-centric approaches marks a significant stride toward a resilient and efficient energy ecosystem. Decision-makers, stakeholders, and researchers may leverage findings for sustainable, adaptive, and consumer-centric energy planning, positioning the sector to address evolving challenges effectively and empowering consumers while maintaining energy efficiency.

Local and global trend Bayesian exponential smoothing models

This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations of additive and multiplicative exponential smoothing models to model series that grow faster than linear but slower than exponential. Their development is motivated by fast-growing, volatile time series. In particular, our models have a global trend that can smoothly change from additive to multiplicative and is combined with a linear local trend. Seasonality, when used, is multiplicative in our models, and the error is always additive but heteroscedastic and can grow through a parameter sigma. We leverage state-of-the-art Bayesian fitting techniques to fit these models accurately, which are more complex and flexible than standard exponential smoothing models. When applied to the M3 competition data set, our models outperform the best algorithms in the competition and other benchmarks, thus achieving, to the best of our knowledge, the best results of per-series univariate methods on this dataset in the literature. An open-source software package of our method is available.

Global Prevalence and Mortality of Type-2 Diabetes from 1990 to 2019, with Future Projections to 2023 and 2050: a Systematic Review

Type-2 diabetes (T2DM) is a key public health issue due to its high prevalence and mortality rate. This review estimates future global trends in T2DM prevalence and mortality using the Forecast exponential smoothing (ETS) model. By 2050, estimated prevalence and mortality for all ages are 10.23% and 2.05 million, respectively, and for adults (>20 years) are 11.97% and 1.9 million. Obesity and high BMI are key risk factors. Mortality is higher in adults due to polypharmacy and complications. T2DM prevalence and mortality will continue to rise alarmingly. Urgent international and national measures are needed to address this burden. Collaboration between researchers, clinicians, and policymakers is crucial for optimal health planning and mitigating the impact of T2DM

Cryptocurrency assets valuation based on statistics model and machine learning

As a matter of fact, cryptocurrency is an exchange media, which uses cryptographic functions to trade, which achieves decentralisation, transparency, and invariance using blockchain. With the rapid development since the first white book proposed in 2008. The market value of cryptocurrency has been exponentially expanded rapidly in recent years. On this basis, programmatic trading strategy are paid more attention by people in trading cryptocurrency contemporarily. With this in mind, this study provides three models, that is, Exponential Smoothing Model (ESM), Autoregressive Integrated Moving Average (ARIMA) model and Long short-term memory (LSTM) model to forecast the future price of cryptocurrency and use sum of square error to analyse the results of these prediction. According to the analysis, LSTM is the best model to forecast the price of cryptocurrency in this article. Overall, machine learning might be more useful to forecast in this fields than traditional statistical models. Investors and researchers in the future could continue to forecast the price of cryptocurrency combined with LSTM model.

Application of Exponential Smoothing Method for Forecasting Spare Parts Inventory at Heavy Equipment Distributor Company

PT. Kobexindo Tractors Tbk holds a significant spare parts inventory to meet their customers' needs. Over the period from 2016 to 2023, the company experienced an average annual loss of Rp. 1,176,438,113, due to the inadequate analysis of spare parts demand, which serves as a reference in the procurement process. To address this issue, this research focuses on developing a model that can generate accurate forecasts for spare parts inventory, particularly Jungheinrich parts, to support appropriate management decisions in the procurement process at the company. The Exponential Smoothing method is chosen for its ability to handle data with fluctuating patterns and trends. This study will compare the Simple Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing methods. The data ratio used in this research is 70% for training data and 30% for testing data. The prototype development is conducted using the Python programming language. The research results indicate that the Holts Winter Exponential Smoothing Model with Multiplicative Seasonality and Multiplicative Trend (Triple Exponential) is the best method among others, as follows: 1) Train RSME (7.082307), a low RSME value on training data indicates that this model has a small prediction error rate on the data used for training. 2) Test MAPE (6.343268), a low MAPE value on test data indicates that this model provides fairly accurate predictions in percentage terms of the actual values. 3) Test RSME Values (23.160521), a sufficiently low RSME value on test data indicates that this model also successfully generalizes well on unseen data.

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.

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.

A Comparative Analysis of Time Series Models for Onion Price Forecasting: Insights for Agricultural Economics

Onion price forecasting plays a critical role in agricultural planning, market stability, and consumer welfare. By predicting onion prices, stakeholders can make informed decisions regarding planting, harvesting, trading, and consumption, mitigating risks and ensuring sustainable supply chains. The present study aims to forecast monthly wholesale onion prices in Bangalore market by using various statistical techniques like Exponential Smoothing, ARIMA, SARIMA, BATS and TBATS models. The time series price data from January 2010 to December 2023 was utilized for the study. Models were trained on 80% of the data and validated on the remaining 20%. The performance of each model was compared based on the two metrics like Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results revealed that the TBATS model was performing better as compared to other six models with less RMSE of 281.30 and MAPE of 0.1544 Additionally, the residuals of the TBATS model exhibited a random distribution. Therefore, the onion prices for January 2024 to December 2024 were forecasted using the TBATS model.

Fueling the Future: A Comprehensive Analysis and Forecast of Fuel Consumption Trends in U.S. Electricity Generation

The U.S. Energy Information Administration (EIA) provides crucial data on monthly and annual fuel consumption for electricity generation. These data cover significant fuels, such as coal, petroleum liquids, petroleum coke, and natural gas. Fuel consumption patterns are highly dynamic and influenced by diverse factors. Understanding these fluctuations is essential for effective energy planning and decision making. This study outlines a comprehensive analysis of fuel consumption trends in electricity generation. Utilizing advanced statistical methods, including time series analysis and autocorrelation, our objective is to uncover intricate patterns and dependencies within the data. This paper aims to forecast fuel consumption trends for electricity generation using data from 2015 to 2022. Several time series forecasting models, including all four benchmark methods (Mean, Naïve, Drift, and seasonal Naïve), Seasonal and Trend Decomposition using Loess (STL), exponential smoothing (ETS), and the Autoregressive Integrated Moving Average (ARIMA) method, have been applied. The best-performing models are determined based on Root Mean Squared Error (RMSE) values. For natural gas (NG) consumption, the ETS model achieves the lowest RMSE of 20,687.46. STL demonstrates the best performance for coal consumption with an RMSE of 5936.203. The seasonal Naïve (SNaïve) model outperforms the others for petroleum coke forecasting, yielding an RMSE of 99.49. Surprisingly, the Mean method has the lowest RMSE of 287.34 for petroleum liquids, but the ARIMA model is reliable for its ability to capture complex patterns. Residual plots are analyzed to assess the models’ performance against statistical parameters. Accurate fuel consumption forecasting is very important for effective energy planning and policymaking. The findings from this study will help policymakers strategically allocate resources, plan infrastructure development, and support economic growth.

Construction of China's food security evaluation index system and spatiotemporal evolution.

Food security is a vital material foundation for a nation's development and has been a topic of significant concern on the international stage in recent years. With a population exceeding 1.4 billion, China is not only a major producer but also a substantial consumer of food. Ensuring food security in China is not only a top priority for its socio-economic development but also a driving force in maintaining the stability of the global food supply chain and reducing the number of hungry people worldwide. However, a lack of comprehensive research into the Chinese food security system remains. This study addresses this gap by constructing a comprehensive evaluation framework encompassing four dimensions: food supply, accessibility, production stability, and sustainability. Utilizing the Moran's Index and generating LISA (Local Indicators of Spatial Association) maps, we analyze the spatial correlations of food security. The Dagum Gini coefficient and kernel density estimation are applied to assess heterogeneity and spatial disparities. Furthermore, this research employs the Exponential Smoothing (ETS) model to forecast food security trends. The findings reveal that the overall composite food security score exhibited fluctuations, initially increasing and reaching its peak of 0.407 in 2003, followed by a subsequent sharp decline after 2019. Spatially, food security exhibits correlations, with the Huang-Huai-Hai Plain and Northeast regions consistently showing high-high clustering. In contrast, the Western and Southern regions exhibit low-low clustering at specific periods. The Dagum Gini coefficient indicates that overall food security disparities are relatively small. However, these disparities have gradually expanded in recent years, with inter-group differences becoming predominant after 2005. As indicated by the kernel density estimation, the dynamic distribution of food security initially widens and then narrows, suggesting a shift from dispersed to concentrated data distribution. This phenomenon is accompanied by polarization and convergence trends, particularly evident after 2015. According to the ETS model, the study forecasts a substantial risk of declining food security in China over the next decade, largely influenced by the ongoing pandemic. In conclusion, this research provides a comprehensive assessment of the changing status of food security in China. It offers early warnings through predictive analysis, addressing the existing research gaps in the field of food security.

RAINFALL FORECASTING WITH AN INTERMITTENT APPROACH USING HYBRID EXPONENTIAL SMOOTHING NEURAL NETWORK

Rainfall forecasting is crucial in agriculture, water resource management, urban planning, and disaster preparation. Traditional approaches fail to capture complicated and intermittent rainfall patterns. The “Hybrid Exponential Smoothing Neural Network” is introduced in this study to handle intermittent rainfall forecasting issues. Exponential Smoothing, an established approach for discovering underlying patterns and seasonal fluctuations in time series data, is combined with Neural Networks, which are good at capturing complex linkages and nonlinearities. Using these two methods, this model hopes to deliver a complete rainfall forecasting solution that accounts for short-term changes and long-term patterns. This research uses residuals from the exponential smoothing model and is modeled using a Neural Network. The residual input is transformed using rolling mean. The results show that the hybrid model is able to capture patterns well, but there are still patterns that experience time lag. Experimental results obtained reveal that the hybrid methodology performs better than the model exponential smoothing, implying that the proposed model hybrid synergy approach can be used as an alternative solution to the rainfall time series forecasting. The results show that the Hybrid method can form patterns better than individual exponential smoothing models or neural networks. The RMSSE values for all areas are 1.0185, 1.55092, 1.0872.