Relevant bibliographies by topics / Exponential forecasting method

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  2. Dissertations / Theses
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Academic literature on the topic 'Exponential forecasting method'

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Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Exponential forecasting method"

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Devira, Annisa Suci, Yuki Novia Nasution, and Suyitno Suyitno. "Peramalan Pendapatan Asli Daerah Kota Samarinda Menggunakan Metode Double Exponential Smoothing Dari Brown." EKSPONENSIAL 14, no. 1 (2023): 41. http://dx.doi.org/10.30872/eksponensial.v14i2.1138.

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Forecasting is a technique for estimating a value in the future by paying attention to past data and current data. One of the forecasting methods for exponentially increasing or decreasing data patterns is Exponential Smoothing. Exponential Smoothing is a method that shows the weighting decreases exponentially with respect to the older observation values. The linear model of the Exponential Smoothing method that uses a two-time smoothing process is Brown's Double Exponential Smoothing method. This study aims to get a forecast of Regional Original Income (PAD) in Samarinda with the double exponential smoothing method. Research data is secondary data from the Samarinda City Regional Revenue Agency (BAPENDA) file. The conclusion of the study is that the results of forecasting PAD in the city of Samarinda in 2021 are IDR 3.374.750.000.000 with an accuracy rate of Mean Absolute Percentage Error (MAPE) of 0,41%.
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Mironov, A. N., S. U. Pirogov, A. I. Petuhov, V. K. Sinilov, and O. L. Shestopalova. "Uneven time series forecasting using a modified exponential smoothing method." E3S Web of Conferences 531 (2024): 03020. http://dx.doi.org/10.1051/e3sconf/202453103020.

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The article is devoted to the problem of forecasting time series with an uneven distribution of observations over time. The exponential smoothing model is used as the basic forecasting model, in which the variable weights of observations decrease exponentially. The exponential smoothing model allows us to take into account the attenuation of the correlation of cross sections of a random process of time series change over time. However, this does not take into account the factors of temporal unevenness of the results of observations and the finiteness of the sample of observations. The article describes a method for predicting an uneven time series based on a modified exponential smoothing model, in which the transition from exponential smoothing to decreasing non-exponential smoothing is carried out. The modified sequence of the weights of the observations is determined by adjusting the classically calculated exponential weights, taking into account the actual irregularity of the observations.
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Deakshinamurthyt. "DEMAND FORECASTING FOR CEMENT IN INDIA 2030." International Journal of Marketing & Financial Management Volume 5, Issue 8, Aug-2017 (2017): pp 09–13. https://doi.org/10.5281/zenodo.888259.

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The paper estimates the demand forecasting for cement in India for 2030 based on exponential demand forecasting, linear forecasting and polynomial method. Cement being an important raw material for construction and infrastructure developments it supports in constructing a better nation. Three different methods for forecasting of cement production is done. The exponential method is suitable for high economic activity in India and polynomial (degree two) for average level of economic activity and linear forecasting for a low level of economic activity in the country.
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Hayuningtyas, Ratih Yulia. "Sistem Informasi Peramalan Persediaan Barang Menggunakan Metode SES Dan DES." Indonesian Journal on Software Engineering (IJSE) 4, no. 1 (2019): 1–6. http://dx.doi.org/10.31294/ijse.v4i1.6228.

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Abstract: Sales is an activity in selling products that provide information about inventory. Arga Medical is a shop engaged in the sale of medical equipment, many of sales transactions in the
 Arga Medical will affect the inventory. Problems in the Arga Medical is predicting many of product that must available for the next month. Therefore this research makes inventory information forecasting system using Single Exponential Smoothing and Double Exponential Smoothing method. This inventory forecasting information system will result a inventory forecasting for next month. Single Exponential Smoothing Method gives equal weight to each data while Double Exponential Smoothing method is smoothing twice. The Data used in this research is the sales data during 2016. Both of these methods resulted inventory forecasting in the next month is Januari 2017 of 52 with Single Exponential Smoothing and 60 with Double Exponential Smoothing. Each method has a Mean Square Error value the smallest error value is the best method for forecasting inventory.
 
 Keywords: Forecasting, Inventory, Single Exponential Smoothing, Double Exponential Smoothing.
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Hasan, M. Babul, and Md Nayan Dhali. "Determination of Optimal Smoothing Constants for Exponential Smoothing Method & Holt’s Method." Dhaka University Journal of Science 65, no. 1 (2017): 55–59. http://dx.doi.org/10.3329/dujs.v65i1.54509.

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This paper concentrates on choosing the appropriate smoothing constants for Exponential Smoothing method and Holt’s method. These two methods are very important quantitative techniques in forecasting. The accuracy of forecasting of these techniques depends on Exponential smoothing constants. So, choosing an appropriate value of Exponential smoothing constants is very crucial to minimize the error in forecasting. In this paper, we have showed how to choose optimal smoothing constants of these techniques for a particular problem. We have demonstrated the techniques by presenting a real life example and calculating corresponding forecast value of these two techniques for the optimal smoothing constants.
 Dhaka Univ. J. Sci. 65(1): 55-59, 2017 (January)
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Wofuru-Nyenke, Ovundah. "Predicting demand in a bottled water supply chain using classical time series forecasting models." Journal of Future Sustainability 2, no. 2 (2022): 65–80. http://dx.doi.org/10.5267/j.jfs.2022.9.006.

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In this paper, various classical time series forecasting methods were compared to determine the forecasting method with the highest accuracy in predicting demand of the 50cl product of a bottled water supply chain. The classical time series forecasting methods compared are the moving average, weighted moving average, exponential smoothing, adjusted exponential smoothing, linear trend line, Holt’s model, and Winter’s model. These methods were evaluated to determine the method with the least Mean Absolute Deviation (MAD) value and hence the highest forecasting accuracy. From the results, the weighted moving average forecasting method had the lowest MAD value of 1,987, making it the forecasting method with the highest accuracy for predicting the 50cl bottled water demand. While the exponential smoothing forecasting method had the highest MAD value of 2,483, making it the forecasting method with the least accuracy for predicting the 50cl bottled water demand. This research provides a procedure for aiding supply chain analysts in implementing demand forecasting using classical time series forecasting models.
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Bas, Eren, Erol Egrioglu, and Ufuk Yolcu. "Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm." Forecasting 3, no. 4 (2021): 839–49. http://dx.doi.org/10.3390/forecast3040050.

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Exponential smoothing methods are one of the classical time series forecasting methods. It is well known that exponential smoothing methods are powerful forecasting methods. In these methods, exponential smoothing parameters are fixed on time, and they should be estimated with efficient optimization algorithms. According to the time series component, a suitable exponential smoothing method should be preferred. The Holt method can produce successful forecasting results for time series that have a trend. In this study, the Holt method is modified by using time-varying smoothing parameters instead of fixed on time. Smoothing parameters are obtained for each observation from first-order autoregressive models. The parameters of the autoregressive models are estimated by using a harmony search algorithm, and the forecasts are obtained with a subsampling bootstrap approach. The main contribution of the paper is to consider the time-varying smoothing parameters with autoregressive equations and use the bootstrap method in an exponential smoothing method. The real-world time series are used to show the forecasting performance of the proposed method.
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Mentari, Maharani Sari Mutiara, and Irwan Iftadi. "Selection of the Best Forecasting Method at PT. Indaco Warna Dunia." Teknoin 28, no. 01 (2023): 1–10. http://dx.doi.org/10.20885/teknoin.vol28.iss1.art1.

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PT. Indaco Warna Dunia is a decorative paint company in Indonesia that produces products under the brands Envi, Belazo, and Top Seal. Preliminary observations revealed that the forecasting method used by the company is ineffective and inaccurate. This inaccurate forecast result company’s problem in fulfilling the demand. This study aims to select the best forecasting method to improve forecast effectiveness and accuracy. The research was conducted at the Tarakan depot, and the products understudy were a fast-moving product category, specifically the Envi brand. Several forecasting methods such as Moving Average, Weighted Moving Average, Single Exponential Smoothing, Exponential Smoothing with Trend, and Holt's Double Exponential Smoothing. The accuracy of forecasting is the most important and it can be measured with MAPE (Mean Absolute Percentage Error). The results showed that Holt's Double Exponential Smoothing method is the best for three products, while the Exponential Smoothing with Trend method, and Single Exponential Smoothing method are the best for one of the products, respectively.
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Andreyanto, Muhammad Faisal, and Hana Catur Wahyuni. "Comparison of Forecasting Techniques Moving Average and Double Exponential Smoothing in Sugar Production for Enhanced Maintenance Preparedness Ahead of Milling Season." Procedia of Engineering and Life Science 7 (March 18, 2024): 620–27. http://dx.doi.org/10.21070/pels.v7i0.1558.

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Sugar as a commodity has a very vital role, not only being a basic need for Indonesian society, but also an inseparable element in various industrial sectors. The aim of this research is to compare sugar production forecasting systems by comparing two methods using moving averages and double exponential smoothing (holt's method). Forecasting is the process of projecting or predicting future events with a structured planning approach. Forecasting in sugar production is needed to find out whether the next month can meet the target or not. The amount of sugar production is used as a reference for carrying out maintenance which is carried out before the arrival of the milling season. The research results show that both forecasting methods have their respective strengths and weaknesses. The results of this research showed that the double exponential smoothing method (Holt's method) provided better values ​​than the moving average.
 Highlights:
 
 Sugar production forecasting aids in maintenance planning.
 Comparison between moving averages and double exponential smoothing methods enhances predictive accuracy.
 The study reveals the superiority of Holt's method in sugar production forecasting.
 
 Keywords: Forecasting, Moving Average, Double Exponential Smoothing, Sugar Production
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Lim, P. Y., and C. V. Nayar. "Solar Irradiance and Load Demand Forecasting based on Single Exponential Smoothing Method." International Journal of Engineering and Technology 4, no. 4 (2012): 451–55. http://dx.doi.org/10.7763/ijet.2012.v4.408.

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Dissertations / Theses on the topic "Exponential forecasting method"

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Simmons, Laurette Poulos. "The Development and Evaluation of a Forecasting System that Incorporates ARIMA Modeling with Autoregression and Exponential Smoothing." Thesis, North Texas State University, 1985. https://digital.library.unt.edu/ark:/67531/metadc332047/.

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This research was designed to develop and evaluate an automated alternative to the Box-Jenkins method of forecasting. The study involved two major phases. The first phase was the formulation of an automated ARIMA method; the second was the combination of forecasts from the automated ARIMA with forecasts from two other automated methods, the Holt-Winters method and the Stepwise Autoregressive method. The development of the automated ARIMA, based on a decision criterion suggested by Akaike, borrows heavily from the work of Ang, Chuaa and Fatema. Seasonality and small data set handling were some of the modifications made to the original method to make it suitable for use with a broad range of time series. Forecasts were combined by means of both the simple average and a weighted averaging scheme. Empirical and generated data were employed to perform the forecasting evaluation. The 111 sets of empirical data came from the M-Competition. The twenty-one sets of generated data arose from ARIMA models that Box, Taio and Pack analyzed using the Box-Jenkins method. To compare the forecasting abilities of the Box-Jenkins and the automated ARIMA alone and in combination with the other two methods, two accuracy measures were used. These measures, which are free of magnitude bias, are the mean absolute percentage error (MAPE) and the median absolute percentage error (Md APE).
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Tran, Thai Thanh, Quang Xuan Ngo, Hieu Hoang Ha, and Nhan Phan Nguyen. "Short-term forecasting of salinity intrusion in Ham Luong river, Ben Tre province using Simple Exponential Smoothing method." Technische Universität Dresden, 2019. https://tud.qucosa.de/id/qucosa%3A70822.

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Salinity intrusion in a river may have an adverse effect on the quality of life and can be perceived as a modern-day curse. Therefore, it is important to find technical ways to monitor and forecast salinity intrusion. In this paper, we designed a forecasting model using Simple Exponential Smoothing method (SES) which performs weekly salinity intrusion forecast in Ham Luong river (HLR), Ben Tre province based on historical data obtained from the Center for Hydro-meteorological forecasting of Ben Tre province. The results showed that the SES method provides an adequate predictive model for forecast of salinity intrusion in An Thuan, Son Doc, and Phu Khanh. However, the SES in My Hoa, An Hiep, and Vam Mon could be improved upon by another forecasting technique. This study suggests that the SES model is an easy-to-use modeling tool for water resource managers to obtain a quick preliminary assessment of salinity intrusion.<br>Xâm nhập mặn có thể gây tác động xấu đến đời sống con người, tuy nhiên nó hoàn toàn có thể dự báo được. Cho nên, một điều quan trọng là tìm được phương pháp kỹ thuật phù hợp để dự báo và giám sát xâm nhập mặn trên sông. Trong bài báo này, chúng tôi sử dụng phương pháp Simple Exponential Smoothing để dự báo xâm nhập mặn trên sông Hàm Luông, tỉnh Bến Tre. Kết quả cho thấy mô hình dự báo phù hợp cho các vị trí An Thuận, Sơn Đốc, và Phú Khánh. Tuy nhiên, các vị trí Mỹ Hóa, An Hiệp, và Vàm Mơn có thể tìm các phương pháp khác phù hợp hơn. Phương pháp Simple Exponential Smoothing rất dễ ứng dụng trong quản lý nguồn nước dựa vào việc cảnh báo xâm nhập mặn.
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Cifonelli, Antonio. "Probabilistic exponential smoothing for explainable AI in the supply chain domain." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR41.

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Le rôle clé que l’IA pourrait jouer dans l’amélioration des activités commerciales est connu depuis longtemps, mais le processus de pénétration de cette nouvelle technologie a rencontré certains freins au sein des entreprises, en particulier, les coûts de mise œuvre. En moyenne, 2.8 ans sont nécessaires depuis la sélection du fournisseur jusqu’au déploiement complet d’une nouvelle solution. Trois points fondamentaux doivent être pris en compte lors du développement d’un nouveau modèle. Le désalignement des attentes, le besoin de compréhension et d’explications et les problèmes de performance et de fiabilité. Dans le cas de modèles traitant des données de la supply chain, cinq questions spécifiques viennent s’ajouter aux précédentes : - La gestion des incertitudes. Les décideurs cherchent un moyen de minimiser le risque associé à chaque décision qu’ils doivent prendre en présence d’incertitude. Obtenir une prévision exacte est un rêve ; obtenir une prévision assez précise et en calculer les limites est réaliste et judicieux. - Le traitement des données entières et positives. La plupart des articles ne peuvent pas être vendus en sous-unités. Cet aspect simple de la vente se traduit par une contrainte qui doit être satisfaite : le résultat doit être un entier positif. - L’observabilité. La demande du client ne peut pas être mesurée directement, seules les ventes peuvent être enregistrées et servir de proxy. - La rareté et la parcimonie. Les ventes sont une quantité discontinue. En enregistrant les ventes par jour, une année entière est condensée en seulement 365 points. De plus, une grande partie d’entre elles sera à zéro. - L’optimisation juste-à-temps. La prévision est une fonction clé, mais elle n’est qu’un élément d’une chaîne de traitements soutenant la prise de décision. Le temps est une ressource précieuse qui ne peut pas être consacrée entièrement à une seule fonction. Le processus de décision et les adaptations associées doivent donc être effectuées dans un temps limité et d’une manière suffisamment flexible pour pouvoir être interrompu et relancé en cas de besoin afin d’incorporer des événements inattendus ou des ajustements nécessaires. Cette thèse s’insère dans ce contexte et est le résultat du travail effectué au cœur de Lokad. La recherche doctorale a été financée par Lokad en collaboration avec l’ANRT dans le cadre d’un contrat CIFRE. Le travail proposé a l’ambition d’être un bon compromis entre les nouvelles technologies et les attentes des entreprises, en abordant les divers aspects précédemment présentés. Nous avons commencé à effectuer des prévisions en utilisant la famille des lissages exponentiels, qui sont faciles à mettre en œuvre et extrêmement rapides à exécuter. Largement utilisés dans l’industrie, elles ont déjà gagné la confiance des utilisateurs. De plus, elles sont faciles à comprendre et à expliquer à un public non averti. En exploitant des techniques plus avancées relevant du domaine de l’IA, certaines des limites des modèles utilisés peuvent être surmontées. L’apprentissage par transfert s’est avéré être une approche pertinente pour extrapoler des informations utiles dans le cas où le nombre de données disponibles était très limité. Nous avons proposé d’utiliser un modèle associé à une loi de Poisson, une binomiale négative qui correspond mieux à la nature des phénomènes que nous cherchons à modéliser et à prévoir. Nous avons aussi proposé de traiter l’incertitude par des simulations de Monte Carlo. Un certain nombre de scénarios sont générés, échantillonnés et modélisés par dans une distribution. À partir de cette dernière, des intervalles de confiance de taille différentes et adaptés peuvent être déduits. Sur des données réelles de l’entreprise, nous avons comparé notre approche avec les méthodes de l’état de l’art comme DeepAR, DeepSSMs et N-Beats. Nous en avons déduit un nouveau modèle conçu à partir de la méthode Holt-Winter [...]<br>The key role that AI could play in improving business operations has been known for a long time, but the penetration process of this new technology has encountered certain obstacles within companies, in particular, implementation costs. On average, it takes 2.8 years from supplier selection to full deployment of a new solution. There are three fundamental points to consider when developing a new model. Misalignment of expectations, the need for understanding and explanation, and performance and reliability issues. In the case of models dealing with supply chain data, there are five additionally specific issues: - Managing uncertainty. Precision is not everything. Decision-makers are looking for a way to minimise the risk associated with each decision they have to make in the presence of uncertainty. Obtaining an exact forecast is a advantageous; obtaining a fairly accurate forecast and calculating its limits is realistic and appropriate. - Handling integer and positive data. Most items sold in retail cannot be sold in subunits. This simple aspect of selling, results in a constraint that must be satisfied by the result of any given method or model: the result must be a positive integer. - Observability. Customer demand cannot be measured directly, only sales can be recorded and used as a proxy. - Scarcity and parsimony. Sales are a discontinuous quantity. By recording sales by day, an entire year is condensed into just 365 points. What’s more, a large proportion of them will be zero. - Just-in-time optimisation. Forecasting is a key function, but it is only one element in a chain of processes supporting decision-making. Time is a precious resource that cannot be devoted entirely to a single function. The decision-making process and associated adaptations must therefore be carried out within a limited time frame, and in a sufficiently flexible manner to be able to be interrupted and restarted if necessary in order to incorporate unexpected events or necessary adjustments. This thesis fits into this context and is the result of the work carried out at the heart of Lokad, a Paris-based software company aiming to bridge the gap between technology and the supply chain. The doctoral research was funded by Lokad in collaborationwith the ANRT under a CIFRE contract. The proposed work aims to be a good compromise between new technologies and business expectations, addressing the various aspects presented above. We have started forecasting using the exponential smoothing family which are easy to implement and extremely fast to run. As they are widely used in the industry, they have already won the confidence of users. What’s more, they are easy to understand and explain to an unlettered audience. By exploiting more advanced AI techniques, some of the limitations of the models used can be overcome. Cross-learning proved to be a relevant approach for extrapolating useful information when the number of available data was very limited. Since the common Gaussian assumption is not suitable for discrete sales data, we proposed using a model associatedwith either a Poisson distribution or a Negative Binomial one, which better corresponds to the nature of the phenomena we are seeking to model and predict. We also proposed using Monte Carlo simulations to deal with uncertainty. A number of scenarios are generated, sampled and modelled using a distribution. From this distribution, confidence intervals of different and adapted sizes can be deduced. Using real company data, we compared our approach with state-of-the-art methods such as DeepAR model, DeepSSMs and N-Beats. We deduced a new model based on the Holt-Winter method. These models were implemented in Lokad’s work flow
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Sulemana, Hisham. "Comparison of mortality rate forecasting using the Second Order Lee–Carter method with different mortality models." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-43563.

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Mortality information is very important for national planning and health of a country. Mortality rate forecasting is a basic contribution for the projection of financial improvement of pension plans, well-being and social strategy planning. In the first part of the thesis, we fit the selected mortality rate models, namely the Power-exponential function based model, the ModifiedPerks model and the Heligman and Pollard (HP4) model to the data obtained from the HumanMortality Database [22] for the male population ages 1–70 of the USA, Japan and Australia. We observe that the Heligman and Pollard (HP4) model performs well and better fit the data as compared to the Power-exponential function based model and the Modified Perks model. The second part is to systematically compare the quality of the mortality rate forecasting using the second order Lee–Carter method with the selected mortality rate models. The results indicate that Power-exponential function based model and the Heligman and Pollard (HP4) model gives a more reliable forecast depending on individual countries.
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Lawton, Richard. "Exponential smoothing methods." Thesis, University of Bath, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340928.

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Choo, Wei-Chong. "Volatility forecasting with exponential weighting, smooth transition and robust methods." Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.489421.

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This thesis focuses on the forecasting of the volatility in financial returns. Our first main contribution is the introduction of two new approaches for combining volatility forecasts. One approach involves the use of discounted weighted least square. The second proposed approach is smooth transition (ST) combining, which allows the combining weights to change gradually and smoothly over time in response to changes in suitably chosen transition variables.
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Skopal, Martin. "Analýza a předpověď ekonomických časových řad pomocí vybraných statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-400475.

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V této diplomové práci se zaměřujeme na vytvoření plně automatizovaného algoritmu pro předpovědi finančních řad, který se snaží využít kombinační proceduru na dvou úrovních mezi dvěma rodinami předpovědních modelů, Box-Jenkins a Exponenciální stavové modely, které jsou schopny modelovat jak homoskedastické tak heteroskedastické časové řady. Pro tento účel jsme navrhli selekční proceduru v prostředí MATLAB pro modely ARIMA. Výsledný kombinovaný model je pak aplikován několik finančních časových řad a jeho výkonost je diskutována.
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Chen, Bo-You, and 陳柏佑. "Exponential weighted methods for forecasting the hourly fine particulate matter (PM2.5) concentrations with seasonal cycle." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/61191264538713150903.

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碩士<br>國立屏東科技大學<br>工業管理系所<br>100<br>Due to people's living standards are improving, the number of motor vehicles grew rapidly, and the impact of industrialization, resulting in urban air quality has become worse. The quality of air has direct relationship with one’s health. Many studies had supported that fine particulates (PM2.5) suspended in the air are harmful to the human respiratory system and could further lead to severe cases of bronchitis. It had become an international trend to use the measurement of these fine particulates as the regulatory strategy of air quality control. Previous studies on fine particles, ozone and other air pollutants had focused on chemical properties of the air contaminants and basic statistical analysis. There are researches that based their prediction on the concentration of contaminants, but only limited to prediction of average concentration of the year, quarter, month, and day. Reports on air quality will aid people who are allergic to air pollutants and patients who suffer from bronchitis to take early precautions. This study used published data obtained from the Environmental Protection Administration website that collected the concentration of PM2.5 at every hour at stations in Ping-Tung city from January 1st, 2007 to December 31st, 2011. The research tried to construct two PM2.5 concentration prediction models based on the concept of moving average. Differed from diffusion and time series models, the research adopted longitudinal viewpoint to sort the historical data. From the time sequence plots, the data suggested that the concentration of PM2.5 cycles hourly though the day; this cycle continues throughout the months. As of result, the seasonal months were considered as an index and the seasonal factors were eliminated. Exponentially weighted moving average (EWMA) model I and II were used to predict the hourly PM2.5 concentration in Ping-Tung city throughout the day. The minimal mean absolute deviation and minimal one-step ahead Mean Squared Prediction Error were used as a criterion to determine the optimal smoothing parameter * in model I and II, respectively. Finally, the hourly PM2.5 concentration in Ping-Tung city from January 1st to April 30st, 2011 were predicted using the EWMA model I and II with the best smoothing parameter. The results showed that predicted values generally consistent with the real values.
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Chang, Chia-I., and 張家翊. "Investigating the Bullwhip Effects of Remanufacturing with the Uncertainties of Return Rate by Using the Forecasting Methods of Exponential Smoothing and Moving Average." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/79089476717274055029.

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碩士<br>中原大學<br>工業與系統工程研究所<br>97<br>In last few decades, electronic and hi-tech products lead people to a new era, but it also produced a huge amount of waste of technological products, so the problems of environmental pollution are getting worse. In order to avoid the continuous damage on environment, countries around the world began to invest in resources for the development of reused products due to the sense of environmental protection progressively, and enterprises can take into account environmental protection and saving natural resources obligations. Reverse logistics is a process of return, re-processing, re-manufacturing and re-sold that let old products from consumers to the markets. Over the years, scholars developed some effective cost models of reverse logistics, so enterprises can increase operating income by saving costs. Due to the research of reverse logistics and remanufacturing developed in these years, most researches of reverse logistics investigated the cost problem of fixed demand, but few of them considered the problem of increasing variation of inventories and bullwhip effect caused by the changes of return rate. This research considers the recovery products and transportation cost factors to calculate the optimal inventory for retailers to extend the forward logistics models, and estimate the order-up-to point in each period of retailer, then forecasting the customer demand by the method of moving average and exponential smoothing to investigate the effects of bullwhip effect under different parameters. This research found that there are significant difference between the two forecasting methods, and the moving average forecasting method can decrease the bullwhip effect in two-echelon supply chain by statistical testing hypothesis.
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Books on the topic "Exponential forecasting method"

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B, Hudak Gregory, ed. Forecasting and time series analysis using the SCA statistical system: Vol. I : Box-Jenkins ARIMA modeling, intervention analysis, transfer function modeling, outlier detection and adjustment, exponential smoothing, related univariate methods. Scientific computing associates corp., 1992.

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Khan, Aman, and Kenneth A. Scorgie. Forecasting Government Budgets. The Rowman & Littlefield Publishing Group, 2022. https://doi.org/10.5040/9781666990355.

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Forecasting is integral to all governmental activities, especially budgetary activities. Without good and accurate forecasts, a government will not only find it difficult to carry out its everyday operations but will also find it difficult to cope with the increasingly complex environment in which it has to operate. This book presents, in a simple and easy to understand manner, some of the commonly used methods in budget forecasting, simple as well as advanced. The book is divided into three parts: It begins with an overview of forecasting background, forecasting process, and forecasting methods, followed by a detailed discussion of the actual methods in Parts I, II, and III. Part I discusses a combination of basic time series models such as percentage average, simple moving average, double moving average, exponential moving average, double as well as triple, simple trend line, time-series with cyclical variation, and time-series regression, with single and multiple independent variables. Part II discusses some of the more advanced, but frequently used time series models, such as ARIMA, regular as well as seasonal, Vector Autoregression (VAR), and Vector Error Correction (VEC). Part III provides an overview of three of the more recent advances in time series models, namely ensemble forecasting, state-space forecasting, and neural network. The book concludes with a brief discussion of some practical issues in budget forecasting.
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Forecasting with Exponential Smoothing: The State Space Approach (Springer Series in Statistics). Springer, 2013.

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Book chapters on the topic "Exponential forecasting method"

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He, Chao, Xiaoli Yan, and Yilang Huang. "Research on the Forecasting of Construction Accidents with the Cubic Exponential Smoothing Method." In Proceedings of the 18th International Symposium on Advancement of Construction Management and Real Estate. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-44916-1_41.

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Dovdon, Enkhzol, Batnyam Battulga, Suvdaa Batsuuri, and Lkhamrolom Tsoodol. "Forecasting of the COVID-19 Spreading in Global Using the Exponential Smoothing Method." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6757-9_14.

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Aljandali, Abdulkader. "Exponential Smoothing and Naïve Models." In Multivariate Methods and Forecasting with IBM® SPSS® Statistics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56481-4_4.

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Sbrana, Giacomo, and Andrea Silvestrini. "Marginalization and aggregation of exponential smoothing models in forecasting portfolio volatility." In Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2342-0_44.

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Mallouhy, Roxane Elias, Christophe Guyeux, Chady Abou Jaoude, and Abdallah Makhoul. "Forecasting the Number of Firemen Interventions Using Exponential Smoothing Methods: A Case Study." In Advanced Information Networking and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99584-3_50.

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Acar, Yavuz, Onur Cetin, Ozgun Burcu Rodopman, Recep Minga, and Hasret Doguer. "Forecasting Demand for Hospital Services." In Advances in Healthcare Information Systems and Administration. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8103-5.ch015.

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Demand forecasting is one of the important issues related to operations management in health sector. Forecasting patient volume in hospitals provides an important input regarding the correct planning of financial resources, human resources, and material resources. In this chapter, the authors first discuss forecasting patient volume in hospital services and then present a case study involving patient volume forecasting for a local hospital in Turkey. Different traditional statistical methods and machine learning methods are applied to both inpatient and outpatient demand from six polyclinics and a surgery room. Results show that damped trend exponential smoothing method outperforms other methods based on overall performance.
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Nan, Tian, and Bo Hu. "Ex-Warehouse Prediction of Power Materials Based on LSTM and Exponential Smoothing." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240398.

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This study delves into forecasting the ex-warehouse volume of electric power materials within a State Grid’s electric power company, leveraging advanced computer algorithmic approaches. At the core of this research is the development of an innovative weighted combined model that effectively synergizes Long Short-Term Memory (LSTM) and the exponential smoothing method. Renowned for its efficiency in handling sequential data, LSTM is combined with the exponential smoothing method, known for its proficiency in capturing trends and seasonal patterns in data sets. The integration of these methodologies allows for the optimization of serial data processing capabilities of LSTM alongside the trend-forecasting accuracy of the exponential method. The result is a significant enhancement in prediction accuracy, achieved through a weighted combination approach that optimizes the model’s predictive performance. This ensures that the company is equipped with high-precision, reliable forecasts, aiding in informed decision-making and effectively addressing the complexities of electric power material demand forecasting. This tailored solution demonstrates a comprehensive understanding of both the operational needs of the company and the intricate nature of computational algorithms in forecasting tasks.
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Mahuzier, Ignacio Aranís, Pablo A. Viveros Gunckel, Rodrigo Mena Bustos, Christopher Nikulin Chandía, and Vicente González-Prida Díaz. "Innovation in Scientific Knowledge Based on Forecasting Assessment." In Advances in Human and Social Aspects of Technology. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7152-0.ch013.

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This chapter presents a study of forecasting methods applicable to the spare parts demand faced by an automotive company that maintains a share of nearly 25% of the automotive market and sells approximately 13,000 parts per year. These parts are characterized by having intermittent demand and, in some cases, low demand, which makes it difficult for such companies to perform well and to obtain accurate forecasts. Therefore, this chapter includes a study of methods such as the Croston, Syntetos and Boylan, and Teunter methods, which are known to resolve these issues. Furthermore, the rolling Grey method is included, which is usually used in environments with short historical series and great uncertainty. In this study, traditional methods of prognosis, such as moving averages, exponential smoothing, and exponential smoothing with tendency and seasonality, are not neglected.
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Hartomo, Kristoko Dwi, Sri Yulianto Joko Prasetyo, Muchamad Taufiq Anwar, and Hindriyanto Dwi Purnomo. "Rainfall Prediction Model Using Exponential Smoothing Seasonal Planting Index (ESSPI) For Determination of Crop Planting Pattern." In Computational Intelligence in the Internet of Things. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7955-7.ch010.

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The traditional crop farmers rely heavily on rain pattern to decide the time for planting crops. The emerging climate change has caused a shift in the rain pattern and consequently affected the crop yield. Therefore, providing a good rainfall prediction models would enable us to recommend best planting pattern (when to plant) in order to give maximum yield. The recent and widely used rainfall prediction model for determining the cropping patterns using exponential smoothing method recommended by the Food and Agriculture Organization (FAO) suffered from short-term forecasting inconsistencies and inaccuracies for long-term forecasting. In this study, the authors developed a new rainfall prediction model which applied exponential smoothing onto seasonal planting index as the basis for determining planting pattern. The results show that the model gives better accuracy than the original exponential smoothing model.
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Raikwar, Aditya R., Rahul R. Sadawarte, Rishikesh G. More, Rutuja S. Gunjal, Parikshit N. Mahalle, and Poonam N. Railkar. "Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method." In Intelligent Systems. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5643-5.ch077.

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The need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.
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Conference papers on the topic "Exponential forecasting method"

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Cishe Fransiska Saputri, Wayan, Farid Fitriyadi, and Hardika Khusnuliawati. "Development of a Web-Based Forecasting System Using the Holt-Winters Exponential Smoothing Method to Improve Accuracy in Predicting Cut Flower Harvest Needs." In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024. https://doi.org/10.1109/icoris63540.2024.10903767.

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Wu, Yu, Kai Xue, Boyu Zhao, et al. "Railway Damage Prediction Using TMCMC-based Markov Hazard Model." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0550.

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&lt;p&gt;The decrease in wear rate, along with increased axle load and train velocity, has shifted the primary railway track damage mechanism from wear to rolling contact fatigue (RCF). Rail damage caused by RCF gradually propagate inward, increasing derailment risks. To address this, a multi-state exponential hazard Markov chain model is proposed for simple and reliable railway damage forecasting. The TMCMC-based sampler enables the model to handle high-dimensional databases, allowing for comprehensive consideration of various factors influencing crack propagation. Case study results demonstrate that the proposed method effectively integrates multiple factors, supporting maintenance decisions and prioritizing railway damage for periodic inspections.&lt;/p&gt;
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Chan, K. Y., T. S. Dillon, J. Singh, and E. Chang. "Traffic flow forecasting neural networks based on exponential smoothing method." In 2011 6th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2011. http://dx.doi.org/10.1109/iciea.2011.5975612.

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Hasmin, Erfan, and Nurul Aini. "Data Mining For Inventory Forecasting Using Double Exponential Smoothing Method." In 2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2020. http://dx.doi.org/10.1109/icoris50180.2020.9320765.

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York, Jason C., and Jeremy M. Gernand. "Ascertainment of the Archetype Statistical Method for Incident Rate Forecasting Through Forecast Performance Evaluations." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-53138.

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The potential benefits of a safety program are generally, only realized after an incident has occurred. Resource allocation in an organization’s safety program has the imperative task of balancing costs and often unrealized benefits. Management can be wary to allocate additional resources to a safety program because it is difficult to estimate the return on investment, especially since the returns are a set of negative outcomes not manifested. One way that safety professionals can provide an estimate of potential return on investment is to forecast how the organizations incident rate can be affected by implementing the different resource allocation strategies, and what the expectation for the incident rate would have been without intervention. Safety professionals often trend the performance of their organization’s safety program by benchmarking incident rates against other organizations. Previous studies have employed different statistical forecasting methods to predict how incident rates will react to changes in resource allocation. This paper analyzes the performance of four statistical forecasting methods employed in previous resource allocation studies along another statistical forecasting method, never before used for incident rate prediction, to ascertain the method that provides the highest level of forecast accuracy. By identifying the most accurate forecasting method, the uncertainty of which method a safety professional should utilize for incident rate prediction is reduced. Incident data from the Mine Safety and Health Administration (MSHA) Part 50, was used to forecast both short and long term incident rates. The performance of each of these forecasting methods were evaluated against one another to determine which method has the highest level of accuracy, lowest bias, and best complexity-adjusted goodness-of-fit metrics. Evaluation of the performance provides indications that the double exponential smoothing statistical forecasting method can provide the most accurate incident rate predictions. Analysis of forecast bias indicated that the error for the double exponential smoothing method is unbiased, within the acceptable range for tracking signal, and had a level of prediction accuracy above 70%. The results of this observational study indicate that the double exponential smoothing method should be the method to consider for incident rate prediction. Consistent use of the same forecasting methodology amongst safety professionals as part of their safety program’s resource allocation process, will allow for more consistent benchmarking of incident rate prediction.
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Hong, Liu, Liu Yu, and Li Lin. "Study on Application of Exponential Smoothing Method to Water Environment Safety Forecasting." In 2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iceee.2010.5661181.

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Abdurrahman, Musab, Budhi Irawan, and Roswan Latuconsina. "Flood Forecasting using Holt-Winters Exponential Smoothing Method and Geographic Information System." In 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC). IEEE, 2017. http://dx.doi.org/10.1109/iccerec.2017.8226704.

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Mumpuni, Retno, Sugiarto, and Rais Alhakim. "Design and Implementation of Inventory Forecasting System using Double Exponential Smoothing Method." In 2020 6th Information Technology International Seminar (ITIS). IEEE, 2020. http://dx.doi.org/10.1109/itis50118.2020.9321038.

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Soni, R. S., and D. Srikanth. "Inventory forecasting model using genetic programming and Holt-Winter's exponential smoothing method." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2017. http://dx.doi.org/10.1109/rteict.2017.8256967.

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Feng, Guo, Liu Chen-Yu, Zhou Bin, and Zhang Su-Qin. "Spares Consumption Combination Forecasting Based on Genetic Algorithm and Exponential Smoothing Method." In 2012 5th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2012. http://dx.doi.org/10.1109/iscid.2012.201.

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