Exponential Smoothing Model Research Articles

Forecasting Ambient Air Temperature for Solar Photovoltaic Panel Predictive Maintenance in Kuala Kangsar, Perak, Malaysia

This study presents a forecasting approach to predict ambient air temperatures for the predictive maintenance of solar photovoltaic (PV) panels in Kuala Kangsar, Perak, Malaysia. Accurate temperature forecasting is critical as ambient air temperature significantly influences solar panel efficiency, performance, and maintenance requirements. A comprehensive dataset spanning 19 years (2005–2023) of hourly solar and weather variables, obtained from the Photovoltaic Geographical Information System (PVGIS), was analysed using advanced smoothing techniques, including exponential smoothing models. The study identified the Damped-Trend Linear Exponential Smoothing model as the most effective method based on Akaike and Bayesian Information Criteria. Forecast results for 2024 demonstrated good predictive accuracy, aiding the optimisation of maintenance schedules and the performance of solar PV systems. The findings underscore the importance of integrating advanced predictive techniques to enhance the sustainability and reliability of renewable energy projects.

The benefits of social insurance system prediction using a hybrid fuzzy time series method

Decision-making in many industries relies heavily on accurate forecasts, including the insurance sector. The Social Insurance System (SIS) in Egypt, operating under a fully funded paradigm, depends on reliable predictions to ensure effective financial planning. This research introduces a hybrid predictive model that combines fuzzy time series (FTS) Markov chains with the tree partition method (TPM) and difference transformation to forecast total pension benefits within Egypt’s SIS. A key feature of the proposed model is its ability to optimize the partitioning process, resulting in the creation of nine intervals that reduce computational complexity while maintaining forecasting accuracy. These intervals were consistently applied across all fuzzy time series models for comparison. The model’s performance is evaluated using established metrics such as MAPE, Thiels’ U statistic, and RMSE. Additionally, prediction interval coverage probability (PICP) and mean prediction interval length (MPIL) are used to assess the quality of prediction intervals, with a 95% prediction interval serving as the baseline. The proposed model achieved a PICP of approximately 95%, indicating well-calibrated prediction intervals, although the MPIL of 424.5 reflects a wider uncertainty range. Despite this, the model balances coverage accuracy and interval precision effectively. The results demonstrate that the proposed model significantly outperforms traditional models like linear regression, ARIMA, and exponential smoothing and conventional FTS models like Song, Chen, Yu, and Cheng by achieving the lowest MAPE with the value of 11.8% for training and 10.65% for testing. This superior performance highlights the model’s reliability and potential applicability to further forecasting tasks in the field of insurance and beyond.

Expected environmental implications of hydrogen fuel cell buses in Australia

ABSTRACT Developing more sustainable public transportation modes has become one of the key elements for mitigating the energy and environmental issues that are related to the global transportation sector, especially high capacity public transportation modes such as bus and subway systems. In this study, the expected environmental implications of Fuel Cell Electric Buses (FCEBs) in Australia during the next 25 years were determined. For this purpose, ARIMA and Exponential Smoothing models were developed to predict the expected GHG emissions and the annual kilometres that will be travelled by buses in 2050. The expected Greenhouse Gas (GHG) emissions produced by buses were determined, based on different scenarios (0% FCEBs penetration, 50% FCEBs penetration, 100% FCEBs penetration). The results of the study concluded that a slight increase in the annual travelled distance by buses in Australia is expected over the next 25 years, with a total increase of 6.9% during the period 2022–2050. Moreover, over time, the environmental implications of FCEBs will become more significant, due to the acceleration in the development of more eco-friendly hydrogen production methods, which could lead to a substantial decrease of up to 89% in GHG emissions, with a 100% transition to FCEBs by 2050 compared to 2022.

Інтелектуальні системи прогнозування демографічних змін та їх вплив на маркетингові стратегії в ІТ-індустрії

The article is devoted to the development of an intelligent system for forecasting demographic changes, which is an important task in the modern world. Traditional analysis of demographic data faces difficulties such as limited access to up-to-date information and a significant amount of unstructured data. The system automates the processing of demographic indicators (fertility, mortality, migration) and their structuring for further analysis and forecasting. The authors have implemented ARIMA and Exponential Smoothing models that allow forecasting population size based on trends and seasonality. Testing of the models for Ukraine, Brazil, and other countries showed that the accuracy of the forecasts depends on the socioeconomic characteristics of each country. ARIMA has proven to be highly accurate in forecasting for stable regions, while Exponential Smoothing adapts to changes in trends. This system provides analysts and governments with an important tool for making informed strategic decisions in the field of population policy, allowing them to take into account complex interrelationships and dynamic trends.

Forecasting Market Activity and Aggregate Demand in China Using Time Series Models

The purpose of this study was to forecast market activity and aggregate demand in China using time series models. Forecasts of market activity (MA) and aggregate demand (AD) provide insights into consumer behavior and economic trends, which are crucial for policymakers and businesses alike. In order to forecast the MA and AD in China, this study uses the time series model, especially the exponential smoothing model. Based on the increase rates of M1 and M2 money supply, respectively, and the difference in increase rates between them. These indicators are necessary to assess MA and AD and reflect changes in money liquidity and people's spending behavior. The results suggest that people will be less willing to spend and more inclined to save in the coming years. This finding suggests that MA may decline as consumers become more cautious and prioritize saving over spending. This trend could be attributed to a variety of factors, including economic uncertainty or changes in monetary policy. The implications of this research are significant because it provides a forecasting framework that can help policymakers and businesses anticipate changes in consumer demand and adjust their strategies accordingly. Understanding these trends can lead to more informed decisions in economic planning and financial forecasting.

Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts

Problem definition: We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory, and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and postlaunch distributional forecasts. Methodology/results: Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product. A prelaunch forecast is an ensemble with equal weights on the candidate models’ forecasts, whereas a postlaunch forecast is an ensemble with weights that evolve according to Bayesian updating. Our approach is part frequentist and part Bayesian, resulting in a novel approach tailored to the demand forecasting challenge. We also introduce a new type of life-cycle or product diffusion model with states that can be updated using exponential smoothing. The trend in this model follows the density of an exponentially tilted Gompertz random variable. For postlaunch forecasting, this model is attractive because it can adapt itself to the most recent changes in a product’s life cycle. We provide closed-form distributional forecasts from our model. In two empirical studies, we show that when the ensemble’s candidate models are all in our new type of exponential smoothing model, this version of the ensemble outperforms several leading approaches in both point and quantile forecasting. Managerial implications: In a data-driven operations environment, our model can produce accurate forecasts frequently and at scale. When quantile forecasts are needed, our model has the potential to provide meaningful economic benefits. In addition, our model’s interpretability should be attractive to managers who already use exponential smoothing and ensemble methods for other forecasting purposes. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0359 .

Forecast of Sugarcane Yield in Chongzuo, Guangxi—LSTM Model Based on Fusion of Trend Yield and Meteorological Yield

Purpose: This paper develops a high-precision yield fusion prediction model for the sugarcane industry in Chongzuo, Guangxi, based on the trend yield and meteorological yield using the long short-term memory (LSTM) model to cope with the multiple factors affecting sugarcane production. Decision support is provided to agricultural producers, policymakers, and supply chain managers so that they can plan resource allocation, market strategies, and policy directions more effectively. Methods: The paper modeled trend yield and weather yield separately to explore the complex relationship between the two in influencing sugarcane production. Trend yields were predicted using the exponential smoothing and multilayer perceptron (MLP) models, while meteorological yields were modeled using stepwise regression. The predicted yields were used again as input variables into the LSTM deep learning network to fit the nonlinear relationship between the two yields. Results: The results showed that (1) the fusion strategy of meteorological yield and MLP trend yield adopted by the model was superior to the fusion strategy of meteorological yield and exponentially smoothed trend yield, achieving a very low mean square error (MSE) of 0.011 and a goodness of fit as high as 0.979, which indicated that the model prediction was highly in agreement with the actual yield, confirming the validity of the method. (2) The prediction curve is basically consistent with the trend of actual sugarcane yield, which predicts that the sugarcane yield in Chongzuo, Guangxi, is expected to maintain a stable and small growth trend in the next eight years. (3) The fusion prediction model proposed in this study provides an accurate and practical solution for sugarcane yield prediction in Chongzuo, Guangxi, with the unique advantage of effectively analyzing and integrating the natural and socio-economic factors affecting the yield, which is of significant reference value for the prediction of sugarcane yield in the local area and even in similar ecoregions.

Forecasting CO2 Emissions from Libya’s Transport Sector

This paper presents an innovative approach to forecast carbon dioxide (CO2) emissions from the transport sector in Libya. The method combines machine learning algorithms with historical data and future estimates. The research built a model that took into account factors such as population growth, rates of car ownership, patterns of fuel consumption and government regulations in order to provide an accurate forecast of carbon dioxide (CO2) emissions over the next decade based on the Global Change Assessment Model (GCAM). The authors used a variety of statistical time series models to forecast future CO2 emissions from Libya's transportation sector. These models included the exponential smoothing model (ESM) and the autoregressive integrated moving average (ARIMA). The ARIMA model outperformed the ESM model, achieving an R2 of 0.931 and a root mean square error (RMSE) of 1.040 Mt CO2. The results of the study found that CO2 emissions from Libya's transport sector could increase by 27.98% and 57.99% in 2030 and 2050, respectively. The study proposed six transportation theories to reduce CO2 emissions from Africa's and Libya's transport sectors. The identified factors encompass price systems, land use planning, eco-driving, electric automobiles, bicycle infrastructure, and telecommuting. The authors also examined the needs to reduce CO2 emissions from Libya’s transport sector in order to meet the International Energy Agency’s ambitious targets for reducing CO2 emissions from the global transport sector. These needs arise due to increasing urbanization, population growth, underinvestment in public transportation infrastructure, and the increasing incidence and severity of heat waves. Additionally, hypothetical scenarios are presented to demonstrate the importance of further reducing CO2 emissions from these sectors to match the projections of global change assessment models.

Top-up Frequency Forecasting for Lao Telecommunication Company Using Time-Series Analysis

The study aimed to forecast the top-up frequency for Lao Telecommunication Company (LTC) customers using time-series analysis. Accurate forecasts help the company optimize resource allocation, improve customer service, and boost revenue by anticipating customer demand. The time-series analysis techniques are employed, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average + exogenous variables (SARIMAX) and exponential smoothing (ETS) models, to analyze historical top-up data. The data is pre-processed, identified patterns, and trained the model to forecast future top-up frequencies. The model's predictions closely aligned with the actual data, indicating its effectiveness. The ARIMA model produced reliable forecasts for short-term top-up frequencies, showing the best performance overall with the lowest RMSE and MAE, and a positive R-squared indicating some ability to explain the data. SARIMAX and EST both perform poorly, with very high RMSE and MAE, and negative R-squared values. The results demonstrated that time-series analysis could enhance decision-making in the telecommunications industry by enabling better forecasting of customer behavior. This helps in optimizing marketing strategies, inventory management, and service availability.

Using ARIMA and ETS models for forecasting water level changes for sustainable environmental management

It is vital to provide useful hydrological forecasts for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among other things. This paper introduces a simple and flexible hydrological time series forecasting framework. Predicting water levels is crucial, given the need for sustainable environmental management. The prognosis should be reasonable to persuade individuals to take proper precautions. While many methods have been developed to predict water levels, here the effectiveness of two approaches to predicting river water levels was assessed. For this purpose, nine years of data were used, which were divided into input data (2014–2021) and validation data (2022), on water levels in the Morava e Binçës river for the Vitia station, in the form of monthly time series records, to identify the best model among those used and to identify the information necessary for water resource management and hazard control. Models Autoregressive Integrated Moving Average(ARIMA) and Error Trend and Seasonality, or Exponential Smoothing (ETS), were examined using the R package to determine the most accurate. The results indicate the applicability of both models, as evidenced by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The predictive analysis based on historical water levels allowed the identification of distinct periods characterized by high and low water levels between 2022 and 2024, which is important for the area in question due to the numerous flood events occurring here.

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.

Time series forecasting of price of the agricultural products using data science

Purpose. The purpose of our article is to research and forecast prices for agricultural products using the example of potato prices based on the most effective models using data science techniques. Methodology / approach. Various forecasting models are explored, starting from baseline models like decomposition and exponential smoothing models to more advanced techniques such as ARIMA, SARIMA, as well as deep learning models including neural network. The data is split into training and testing sets, and models are validated using cross-validation techniques and optimised through hyperparameter tuning. Model performance is evaluated using metrics such as MAE, MSE, RMSE, and MAPE. The selected model is then used to generate future price forecasts, with uncertainty quantified through confidence intervals. Results. The study successfully applied advanced data science techniques to forecast potato prices, leveraging a range of effective models. By analysing historical price data and using various forecasting methods, the research identified the most accurate models for predicting future price trends. The results demonstrate that the selected models can provide reliable forecasts. In particular, the results showed that the model could achieve good forecast results when applied to real problems and, thus, can be effectively used for forecasting tasks especially considering seasonality. In addition, it should be noted that the model has a higher prediction accuracy at the time intervals closest to the original data. The obtained results support using both models simultaneously for forecasting, which can compensate for the shortcomings of each of them. The models can be used separately, to more accurately predict the values for the required period, or a combination of them is also possible. Originality / scientific novelty. The study’s originality lies in development of methods for effectively accounting for seasonality in agricultural price data, such as using seasonal decomposition techniques or more advanced techniques that combine statistical and data science approaches. The novelty implies the implementation of real-time data processing and forecasting system allows for the timely prediction of price changes, enabling stakeholders to make more informed decisions. Practical value / implications. Forecasting potato prices holds significant practical value for various stakeholders. For farmers, accurate forecasts enable informed decisions on the optimal times to plant, harvest, and sell their crops, thereby optimising their profits. In the supply chain, distributors and retailers can use these forecasts to manage inventory more effectively and plan contracts, reducing waste and avoiding shortages. Policymakers benefit from forecasts by anticipating market fluctuations and stabilising prices, which supports both consumers and producers. For consumers, stable pricing ensures better budgeting and helps avoid sudden price spikes, making essential foods more affordable. Overall, accurate price forecasting enhances market efficiency by reducing uncertainty and aiding investors in managing risk.

Time series decomposition of land surface temperature for long-term trend forecasting and impact on nesting sea turtle habitats in the Arabian Gulf

Abstract Improving Land Surface Temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as on important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats' long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, SEATS, and the Seasonal and Trend decomposition using Loess (STL) approach. The long-term trends in LST data are driven by climate change rather than seasonal fluctuations. We employed Neural Network Auto Regression (NNAR), BaggedETS, Exponential Smoothing models, and STL method to project future LST values. We also explored advanced forecasting models like Dynamic Harmonic Regression, TBATS, and SARIMA for comparative performance analysis. Extended warm periods were identified for Abu Ali Island between 2017 and 2018 through several decomposition methods, likely linked to the 2015-2016 El Niño event. We also conducted a Marine Heat Wave (MHW) analysis from 2010-2020, establishing a pronounced impact of the 2015-2016 El Niño on the Arabian Gulf. In nesting beach environments with high LST, marine heatwaves could have a significant impact on sea turtle populations without human intervention such as artificially cooling the nest temperature. SARIMA model showed higher forecasting precision for in-situ weather data while NNAR model demonstrated superior performance with remotely sensed data.

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.

Oil-price forecasting based on ARIMA, exponential smoothing, and autoregressive neural network models

Oil prices have an impact on the global economy. Demand shocks, such as those caused by the recent COVID-19 epidemic, or supply shocks caused by production reductions by major global producers, result in sudden price changes. Oil industry has been collecting price data since the late 19th century. A time series of this magnitude allows for consistent analysis of price behavior forecasts following demand or supply shocks. Financial time series are sensitive to exogenous shocks. From this perspective, this work presents a comparative analysis of predictive crude oil prices scenarios, obtained from a historical series of average annual prices. Two approaches were used: first, a combination of classic strategies based on exponential smoothing, and ARIMA models. Second, an autoregressive neural network model. Both approaches are complementary when used for long-term forecasting of oil prices and show good response to volatile data. Therefore, we are able to present an alternative data analysis, in a field where there is a great amount of relevant historical series, using probabilistic and non-linear models in order to observe predictions and make more effective decisions.

Development and Comparison of InSAR-Based Land Subsidence Prediction Models

Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction.

Forecasting of Monthly Crude Oil Prices in Kenya Using Comparative Time Series Models

Crude oil is one of the most vital products that ever existed and variation in prices affects all sectors of the economy and variation in its prices is very crucial. Therefore, without an accurate and appropriate predictive model for crude oil prices, it has proven difficult to predict future oil prices. Therefore, appropriate modeling is crucial for the oil companies to adjust strategies used in the production and supply as well as structural optimization. This study sought to fit several time series forecasting models namely; ARIMA, Naïve, Seasonal Naïve, Time Series Linear Model (TSLM), and Exponential Smoothing (ETS) to model and forecast crude oil prices. The study used Kenya’s monthly crude oil prices from Jan 2003 to Dec 2023, giving a sample of 252 observations. The selection of the best model was based on the minimization of the information criteria, where ARIMA, ETS, and Seasonal Naïve attained an AIC of 1294.193, 1846.780, and 1403.821, respectively. Similarly, the models attained the BIC of 1304.991, 1863.071, and 1450.489, respectively. Since the Naïve and TSLM could not provide the AIC and BIC, model selection was entirely based on the forecast accuracy measures. From the results, the Naive model reported the lowest RMSE (19.6329), indicating that it has the smallest average squared error, with the lowest MAE (15.5212), suggesting it has the smallest average absolute error. Besides, the Naive model reported an ME (1.9819) which is relatively low but not the lowest. The automatic ETS model has a slightly lower ME (3.478211). The naive model reported lower MPE and MAPE values (-3.9525 and 26.8022, respectively) compared to most models, indicating less percentage error. Similarly, a lower MASE and RMSSE were reported by the naïve model, (0.7343624) and RMSSE (0.7228792), respectively, indicating that the model performs well relative to forecasting crude oil prices in Kenya. The naïve model demonstrated a higher consistency and reliability in forecasting crude oil prices in Kenya.

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

Relying on Holt’s linear exponential smoothing model forecasts to inform the national HIV policy in Eswatini

Relying on Holt’s linear exponential smoothing model forecasts to inform the national HIV policy in Eswatini

Prediction of HIV prevalence among individuals aged 15-49 years in Eritrea using Holt’s linear exponential smoothing model

Prediction of HIV prevalence among individuals aged 15-49 years in Eritrea using Holt’s linear exponential smoothing model