Relevant bibliographies by topics / Exponential forecasting method

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Exponential forecasting method'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 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 expon
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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
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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
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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 exam
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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 wei
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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 instea
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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
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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
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Kılıç Topuz, Bakiye, Mehmet BOZOĞLU, NEVRA ALHAS EROĞLU, and Uğur BAŞER. "Forecasting of Onion Sown Area and Production in Turkey Using Exponential Smoothing Method." Turkish Journal of Forecasting 03, no. 2 (2019): 39–46. http://dx.doi.org/10.34110/forecasting.660377.

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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
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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 forec
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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 e
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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
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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 st
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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 retu
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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 metho
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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 a
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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 methodolo
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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 Gre
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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 an
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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-
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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 demonstr
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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 af
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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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