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Journal articles on the topic 'Forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Суворов, Anatoliy Suvorov, Ивантер, Viktor Ivantyer, Сутягин, and Valyeriy Sutyagin. "The Main Objectives and Principles of Socio-Economic Forecasting." Administration 3, no. 1 (2015): 8–17. http://dx.doi.org/10.12737/8785.

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The methodological foundations of socio-economic forecasting are considered in this paper. The forecasting’s definition is given; the forecasting’s role and place in national economy regulation are considered. Forecasts types’ classification and the forecasting’s basic principles have been presented. Forecasting models’ structure and classification, as well as socio-economic forecasts’ elements and development stages have been considered.

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2

Rodrigues, Aaron. "Food Sales Forecasting Using Machine Learning Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 869–72. http://dx.doi.org/10.22214/ijraset.2021.38069.

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Abstract: Food sales forecasting is concerned with predicting future sales of food-related businesses such as supermarkets, grocery stores, restaurants, bakeries, and patisseries. Companies can reduce stocked and expired products within stores while also avoiding missing revenues by using accurate short-term sales forecasting. This research examines current machine learning algorithms for predicting food purchases. It goes over key design considerations for a data analyst working on food sales forecasting’s, such as the temporal granularity of sales data, the input variables to employ for fore

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3

Zheng, Xiao Xia, and Fu Yang. "Research of Wind Speed and Wind Power Forecasting." Advanced Materials Research 347-353 (October 2011): 611–14. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.611.

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Wind power has entered a rapid progress stage. Due to the intermittency of wind energy and the non-linearity of power system, there exist many uncertain variables which should be considered in the wind power prediction. Accurate wind power forecastings are beneficial for wind plant operators, utility operators, and utility customers. The current forecasting methods include persistence method, physical method, statistical method, and the comprehensive one combing all the other methods. This paper provides a detail review on wind speed and wind power forecasting methods based on recent available

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4

Reddy, Dr T. Koti. "Exchange Rate Forecasting." Indian Journal of Applied Research 1, no. 6 (2011): 120–24. http://dx.doi.org/10.15373/2249555x/mar2012/41.

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5

Rathnayaka, R. M. Kapila Tharanga, D. M. K. N. Seneviratna, and Wei Jianguo. "Grey system based novel approach for stock market forecasting." Grey Systems: Theory and Application 5, no. 2 (2015): 178–93. http://dx.doi.org/10.1108/gs-04-2015-0014.

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Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been foc

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6

BAŞER, Uğur, Mehmet BOZOĞLU, Nevra ALHAS EROĞLU, and Bakiye KILIÇ TOPUZ. "Forecasting Chestnut Production and Export of Turkey Using ARIMA Model." Turkish Journal of Forecasting 02, no. 2 (2018): 27–33. http://dx.doi.org/10.34110/forecasting.482789.

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Arabi Belaghi, Reza, Minoo Aminnejad, and Özlem Gürünlü Alma. "Stock Market Prediction Using Nonparametric Fuzzy and Parametric GARCH Methods." Turkish Journal of Forecasting 02, no. 1 (2018): 1–8. http://dx.doi.org/10.34110/forecasting.420126.

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8

Zewdie, Mulugeta Aklilu, Gebretsadik G. Wubit, and Amare W. Ayele. "G-STAR Model for Forecasting Space-Time Variation of Temperature in Northern Ethiopia." Turkish Journal of Forecasting 02, no. 1 (2018): 9–19. http://dx.doi.org/10.34110/forecasting.437599.

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9

Kılıç Topuz, Bakiye, Mehmet Bozoğlu, Uğur Başer, and Nevra Alhas Eroğlu. "Forecasting of Apricot Production of Turkey by Using Box-Jenkins Method." Turkish Journal of Forecasting 02, no. 2 (2018): 20–26. http://dx.doi.org/10.34110/forecasting.482914.

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10

MACİT, İrfan. "Estimating Risk Pressure Factor (RPF) with Artificial Neural Network (ANN) to Locate Search and Rescue (SAR) Team Station." Turkish Journal of Forecasting 03, no. 1 (2019): 26–38. http://dx.doi.org/10.34110/forecasting.484765.

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11

Ustundag Siray, Gülesen, Barnabe Ndabashinze, and Luca Scrucca. "Genetic Algorithms Applied to Fractional Polynomials for Power Selection: Application to Diabetes Data." Turkish Journal of Forecasting 03, no. 1 (2019): 15–25. http://dx.doi.org/10.34110/forecasting.514761.

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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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13

Ürgenç, Sergül, and Barış Aşıkgil. "Bitcoin Trend Reversal Prediction with Tree-Based Ensemble Machine Learning." Turkish Journal of Forecasting 08, no. 01 (2024): 13–22. http://dx.doi.org/10.34110/forecasting.1390292.

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In recent years, Bitcoin (BTC) has become the most popular digital asset in the cryptocurrency market. Its prices are highly volatile due to rapidly increasing investor interest, making it difficult to predict price movements. The aim of this study is to predict trend reversals in BTC price movements by using tree-based ensemble machine learning techniques and compare the success rates of these techniques. For this purpose, the study focuses on points where the trend changes. The ‘buy’, ‘sell’, and ‘hold’ classes are balanced through under-sampling. Extreme Gradient Boosting (XGB), Random Fore

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14

Aksakal, Saime Şule, and Erol Eğrioğlu. "A Resarch on the Factors Affecting the Outcomes of Child Abuse Cases Using Machine Learning Methods." Turkish Journal of Forecasting 9, no. 1 (2025): 17–29. https://doi.org/10.34110/forecasting.1662920.

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Modern information technology makes it possible to collect and store scientific and social research data. Some statistical methods can provide quite reliable results when the necessary assumptions are met in uncovering existing or hidden relationships between data. However, since data collected from real life often do not meet these assumptions, data mining methods that require fewer assumptions and can be applied to flexible and complex data sets have been developed for prediction. The use of machine learning methods, which include data mining techniques, to process data and produce meaningfu

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15

Hitam, Nor Azizah, and Amelia Ritahani Ismail. "Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (2018): 1121. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp1121-1128.

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Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a res

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Nor, Azizah Hitam, and Ritahani Ismail Amelia. "Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (2018): 1121–28. https://doi.org/10.11591/ijeecs.v11.i3.pp1121-1128.

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Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a res

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17

Hasan, M. Babul, M. Asadujjaman, and M. Hasibul Haque. "An Integrated Forecasting Technique with Modified Weight Measurement." Dhaka University Journal of Science 71, no. 1 (2023): 36–41. http://dx.doi.org/10.3329/dujs.v71i1.65270.

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Forecasting has long been part of our life since early of the history of human being. In the middle of 20th century forecasting becomes a part of every business and financial sectors. Nowadays every successful firm has to make its own forecasts with an acceptable error as there is no chance of zero error. The situation becomes more complicated if the observed data is more diverted from the existing pattern. In such situation it becomes more difficult to fit it into a suitable forecasting model.Then it requires to combine several forecasts to reach a better forecast.In this paper, we willdevelo

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18

Castle, Jennifer L., Jurgen A. Doornik, and David F. Hendry. "Forecasting Principles from Experience with Forecasting Competitions." Forecasting 3, no. 1 (2021): 138–65. http://dx.doi.org/10.3390/forecast3010010.

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Economic forecasting is difficult, largely because of the many sources of nonstationarity influencing observational time series. Forecasting competitions aim to improve the practice of economic forecasting by providing very large data sets on which the efficacy of forecasting methods can be evaluated. We consider the general principles that seem to be the foundation for successful forecasting, and show how these are relevant for methods that did well in the M4 competition. We establish some general properties of the M4 data set, which we use to improve the basic benchmark methods, as well as t

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19

Chindia, E. W. "Forecasting Techniques and Accuracy of Performance Forecasting." International Journal of Management Excellence 7, no. 2 (2016): 813. http://dx.doi.org/10.17722/ijme.v7i2.262.

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20

Chindia, E. W. "Forecasting Techniques and Accuracy of Performance Forecasting." International Journal of Management Excellence 7, no. 2 (2016): 813–20. http://dx.doi.org/10.17722/ijme.v7i2.851.

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This article explores the impact of the different forecasting methods (FMs) on the accuracy of performance forecasting (APF) in large manufacturing firms (LMFs), in Kenya. The objective of the study was to assess if the different forecasting methods have an influence on any of the aspects of measures of APF. APF, in manufacturing operations, is seldom derived accurately. However, LMFs tend to hire skilled forecasters, to a great extent, to ensure APF when preparing future budgets. The different types of forecasting techniques have been known to influence the behavior of operations resulting in

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21

Sangrody, Hossein, Morteza Sarailoo, Ning Zhou, Nhu Tran, Mahdi Motalleb, and Elham Foruzan. "Weather forecasting error in solar energy forecasting." IET Renewable Power Generation 11, no. 10 (2017): 1274–80. http://dx.doi.org/10.1049/iet-rpg.2016.1043.

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22

Lim, Joa Sang, and Marcus O'Connor. "Judgmental forecasting with interactive forecasting support systems." Decision Support Systems 16, no. 4 (1996): 339–57. http://dx.doi.org/10.1016/0167-9236(95)00009-7.

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23

N. Kallianiotis, Dr Ioannis. "EXCHANGE RATE FORECASTING: THE FUNDAMENTAL FORECASTING MODEL." International Journal of Research In Commerce and Management Studies 05, no. 05 (2023): 24–58. http://dx.doi.org/10.38193/ijrcms.2023.5502.

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This paper is using the fundamental forecasting model, which is a monetarist theory of exchange rate determination, for the current forecasting. This theory is tested empirically by using data, spot and forward rates and a variety of macro-variables from seven different countries with respect the U.S., as our domestic country. A GARCH-M model is used to forecast the volatility of the spot exchange rate. The paper is also using a Vector Auto-regression (VAR) framework to forecast simultaneously spot (s_t) and forward (f_t) exchange rates by utilizing exogenous macro-variables, time trends, and

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24

Loreño, Dustin, and Aimee Olpenda. "Comparative Analysis of Forecasting Techniques for Enhancing Coconut Oil Export Predictions in the Philippines." Frontier Management Science 1, no. 2 (2024): 14–28. http://dx.doi.org/10.53893/fms.v1i2.267.

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In the agricultural domain, the accurate forecasting of crop yields is crucial for economic stability and planning. The Philippines, being one of the world’s largest producers of coconut oil, has a significant portion of its agricultural sector influenced by the predictability of this commodity’s yield. While traditional forecasting methods have been employed, their accuracy fluctuates, necessitating the exploration of more reliable techniques. This study evaluates Grey Forecasting, Moving Average, Forecast by Forecasting Sheet, and Regression Analysis methods for predicting coconut oil produc

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25

M.THANGAM, M. THANGAM, and Dr P. KARTHIKEYAN Dr.P.KARTHIKEYAN. "Retail Forecasting in India." Paripex - Indian Journal Of Research 3, no. 6 (2012): 103–4. http://dx.doi.org/10.15373/22501991/june2014/32.

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26

Zhang, Haipeng, and Hua Luo. "An Advanced Hybrid Forecasting System for Wind Speed Point Forecasting and Interval Forecasting." Complexity 2020 (November 21, 2020): 1–16. http://dx.doi.org/10.1155/2020/7854286.

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Ultra-short-term wind speed prediction can assist the operation and scheduling of wind turbines in the short term and further reduce the adverse effects of wind power integration. However, as wind is irregular, nonlinear, and nonstationary, to accurately predict wind speed is a difficult task. To this end, researchers have made many attempts; however, they often use only point forecasting or interval forecasting, resulting in imperfect prediction results. Therefore, in this paper, we developed a prediction system integrating an advanced data preprocessing strategy, a novel optimization model,

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27

Sunny, Mathew, and Krishnan S. Ram. "Study of Sales Forecasting Accuracy using ARIMA Model." MERC Global's International Journal of Management, 8, no. 2 (2020): 40–46. https://doi.org/10.35620/IJM.2020.8201.

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Forecasting is the program of action that entails an objective study of the past, present and future, to best estimate what that future holds in the way of sales for any given product or firm. One of the earliest recorded attempts at quantitative forecasting was that of John H. Patterson for the National Cash Register Company in 1887. Forecasting models have been widely investigated by researchers and practitioners. Here to understand the accuracy of forecasting, a study was conducted at Tata Global Beverages Limited (TGBL). The sample size for the study consists of the sales for the tea brand

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28

Vito Eka Perdana Putra, Alfonsus, Yosep Agus Pranoto, and Suryo Adi Wibowo. "PENERAPAN METODE SINGLE EXPONENTIAL SMOOTHING DALAM MERAMAL PENJUALAN DI TOKO AGUNG." JATI (Jurnal Mahasiswa Teknik Informatika) 6, no. 2 (2023): 1065–71. http://dx.doi.org/10.36040/jati.v6i2.5440.

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Peramalan dijadikan untuk pertimbangan dalam bidang bisnis dan ekonomi, agar kerugian perusahaan menjadi sekecil mungkin supaya keuntungan yang di dapat maksimal. Peneliti ini bertujuan untuk membuat sebuah aplikasi Forecastin penjualan berbasis website agar dapat memudahkan Toko Agung dalam meramalkan penjualan. Penelitian untuk mencari solusi agar memudahkan Pemilik Toko Agung untuk Peramalan penjualan barang. Mengunakan Metode Single Exponential Smoothing. Kebutuhan data skripsi yang akan digunakan adalah data penjualan barang Toko Agung pada tahun 2019-2021. Hasil penelitian be

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29

Foster, Ian, M. G. Anderson, and T. P. Burt. "Hydrological Forecasting." Transactions of the Institute of British Geographers 11, no. 4 (1986): 501. http://dx.doi.org/10.2307/621948.

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30

French, Simon, G. Wright, and P. Ayton. "Judgemental Forecasting." Journal of the Operational Research Society 38, no. 12 (1987): 1218. http://dx.doi.org/10.2307/2582755.

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31

Dave, Upendra, J. Holton Wilson, and Barry Keating. "Business Forecasting." Journal of the Operational Research Society 42, no. 3 (1991): 254. http://dx.doi.org/10.2307/2583315.

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32

Chasan, Rebecca. "Phytochemical Forecasting." Plant Cell 6, no. 1 (1994): 3. http://dx.doi.org/10.2307/3869670.

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33

Clark, Mary E. "Economic Forecasting." Science 246, no. 4926 (1989): 10. http://dx.doi.org/10.1126/science.246.4926.10.b.

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34

Lieberman, Bernhardt. "Economic Forecasting." Science 246, no. 4926 (1989): 10. http://dx.doi.org/10.1126/science.246.4926.10.a.

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35

Tarasevych, Yu V. "CRIMINOLOGICAL FORECASTING." Juridical scientific and electronic journal, no. 8 (2020): 565–67. http://dx.doi.org/10.32782/2524-0374/2020-8/141.

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36

Mayer, Jörg H., Milena Meinecke, and Armin Fehr. "Rethink Forecasting." Controlling & Management Review 66, no. 2 (2022): 56–61. http://dx.doi.org/10.1007/s12176-022-0447-4.

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37

Siviero, Stefano, and Daniele Terlizzese. "Macroeconomic Forecasting." Journal of Business Cycle Measurement and Analysis 2007, no. 3 (2008): 287–316. http://dx.doi.org/10.1787/jbcma-v2007-art14-en.

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38

Gneiting, Tilmann, and Matthias Katzfuss. "Probabilistic Forecasting." Annual Review of Statistics and Its Application 1, no. 1 (2014): 125–51. http://dx.doi.org/10.1146/annurev-statistics-062713-085831.

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39

Kumar, Kuldeep. "Demographic Forecasting." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174, no. 1 (2011): 240–41. http://dx.doi.org/10.1111/j.1467-985x.2010.00676_3.x.

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40

CLARK, M. E. "Economic Forecasting." Science 246, no. 4926 (1989): 10. http://dx.doi.org/10.1126/science.246.4926.10-a.

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41

Ord, Keith, and Sam Lowe. "Automatic Forecasting." American Statistician 50, no. 1 (1996): 88. http://dx.doi.org/10.2307/2685050.

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42

Yeoman, Ian. "Forecasting forward." Journal of Revenue and Pricing Management 13, no. 6 (2014): 411–12. http://dx.doi.org/10.1057/rpm.2014.33.

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43

Pielke Jr., R. A. "Hurricane Forecasting." Science 284, no. 5417 (1999): 1123c—1123. http://dx.doi.org/10.1126/science.284.5417.1123c.

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44

Ore, H. T. "Seismic forecasting." IEEE Potentials 9, no. 2 (1990): 19–22. http://dx.doi.org/10.1109/45.52996.

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45

Rothschild, David. "Forecasting Elections." Public Opinion Quarterly 73, no. 5 (2009): 895–916. http://dx.doi.org/10.1093/poq/nfp082.

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46

Vogelsang, Timothy J. "Economic Forecasting." Journal of the American Statistical Association 96, no. 453 (2001): 339–55. http://dx.doi.org/10.1198/jasa.2001.s386.

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47

Chatfield, Chris. "Teaching Forecasting." Teaching Statistics 18, s1 (1997): 12–13. http://dx.doi.org/10.1111/j.1467-9639.1997.tb00857.x.

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48

Chinnavornrungsee, Perawut, Nuwong Chollacoop, Sasiwimon Songtrai, Kobsak Sriprapha, Jun Yoshino, and Tomonao Kobayashi. "Comparison of ensemble forecastings of solar irradiance with different number of ensemble members." Japanese Journal of Applied Physics, April 4, 2025. https://doi.org/10.35848/1347-4065/adc937.

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Abstract For appropriate management of electric power grids connected with photvoltaic (PV) systems, probability prediction of solar irradiance is proposed. Lagged Average Forecast (LAF) method is used for ensemble forecasting. 72-hour ahead forecasting of solar irradiance with a numerical weather prediction (NWP) model is operated in Thailand. Two different ensemble forecastings with NWP are erformed. One is three members, and interval of forecasting cycle is 24-hour, and the other is 12 members, and the interval is 6-hour. Both forecastings work properly, and actual frequencies of forecastin

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49

Kölemen, Emine. "Forecasting of Turkey's Hazelnut Export Amounts According to Seasons with Dendritic Neuron Model Artificial Neural Network." Turkish Journal of Forecasting, June 12, 2024. http://dx.doi.org/10.34110/forecasting.1468420.

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It is seen that artificial neural networks have begun to be used extensively in the literature in solving the time series forecasting problem. In addition to artificial neural networks, classical forecasting methods can often be used to solve this problem. It is seen that classical forecasting methods give successful results for linear time series analysis. However, there is no linear relationship in many time series. Therefore, it can be thought that deep artificial neural networks, which contain more parameters but create more flexible non-linear model structures compared to classical time s

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50

ASER, Daud Ali, and Esin FİRUZAN. "Impact of Structural Break Location on Forecasting Accuracy: Traditional Methods Versus Artificial Neural Network." Turkish Journal of Forecasting, December 30, 2022. http://dx.doi.org/10.34110/forecasting.1162548.

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Since forecasting the future values is fundamental for researchers, investors, practitioners, etc., obtaining accurate predictions is critical in time series analysis. The accuracy is reliant on good modeling and good quality data. The latter is affected by unusual observations, changes over time, missing data, and structural breaks among others. Economic crises are the major cause of data instability and therefore, this paper focuses on how structural breaks in conditional heteroscedastic financial and macroeconomic data affect forecasting accuracy on short and long-term horizons. More specif

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