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Journal articles on the topic 'Forecasting'

To see the other types of publications on this topic, follow the link: Forecasting.

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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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 (March 17, 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

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 published papers. Several forecasting models were discussed and a lot of researchers on the models, which have their own characteristics, were presented. An overview of comparative analysis of wind forecasting time scales is discussed as well.

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3

Rodrigues, Aaron. "Food Sales Forecasting Using Machine Learning Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 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 forecasting sales, and the representation of the sales output variable. It also examines machine learning algorithms that have been used to anticipate food sales and the proper metrics for assessing their performance. Finally, it goes over the major problems and prospects for applied machine learning in the field of food sales forecasting. Keywords: Food, Demand forecasting, Machine learning, Regression, Timeseries forecasting, Sales prediction

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4

Reddy, Dr T. Koti. "Exchange Rate Forecasting." Indian Journal of Applied Research 1, no. 6 (October 1, 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 (August 3, 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 focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose. Findings – The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings. Practical implications – Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches. Originality/value – However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.

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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 (December 31, 2018): 27–33. http://dx.doi.org/10.34110/forecasting.482789.

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7

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 (September 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 (September 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 (December 31, 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 (August 31, 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 (August 31, 2019): 15–25. http://dx.doi.org/10.34110/forecasting.514761.

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12

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 (December 31, 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 (March 27, 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 Forest (RF) and Random Trees (RT) models are developed. The results are evaluated by using precision, recall, specificity, F1 score and accuracy metrics. The study concludes that the XGB model exhibits higher success compared to other models.

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14

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 (September 1, 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 result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

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15

M.THANGAM, M. THANGAM, and Dr P. KARTHIKEYAN Dr.P.KARTHIKEYAN. "Retail Forecasting in India." Paripex - Indian Journal Of Research 3, no. 6 (January 15, 2012): 103–4. http://dx.doi.org/10.15373/22501991/june2014/32.

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16

Castle, Jennifer L., Jurgen A. Doornik, and David F. Hendry. "Forecasting Principles from Experience with Forecasting Competitions." Forecasting 3, no. 1 (February 23, 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 the Card method that we created for our submission to that competition. A data generation process is proposed that captures the salient features of the annual data in M4.

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17

Chindia, E. W. "Forecasting Techniques and Accuracy of Performance Forecasting." International Journal of Management Excellence 7, no. 2 (August 21, 2016): 813. http://dx.doi.org/10.17722/ijme.v7i2.262.

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Chindia, E. W. "Forecasting Techniques and Accuracy of Performance Forecasting." International Journal of Management Excellence 7, no. 2 (August 31, 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 the formulation of either accurate or inaccurate forecasts resulting in either adverse or favorable organizational performance. The study used the three known forecasting methods, objective, subjective and combined forecasting techniques against measures of APF, expected value, growth in market share, return on assets and return on sales. Regression analysis was used applying data collected through a structured questionnaire administered among randomly selected LMFs. Results indicated that there was evidence that APF is influenced by each of the forecasting methods in different ways.

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19

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 (July 11, 2017): 1274–80. http://dx.doi.org/10.1049/iet-rpg.2016.1043.

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20

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

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21

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 policy instruments. Further, at the end an impulse response function and a Hodrick-Prescott filter are used to present visually the behavior of the spot exchange rate. The countries used in the empirical work are, U.S. with respect the Euro-zone, Mexico, Canada, U.K., Switzerland, Japan, and Australia. The results show that these methods are giving very good forecasting for these seven exchange rates by minimizing the standard error of the regression (SER) and the root mean squared error (RMSE). Of course, uncertainty exists always in the forecasting of any economic variables, due to unanticipated public policies (monetary, fiscal, and trade) and other “innovations” in our financial markets, plus the new philosophies (i.e., liberalism, lack of ethics, perversions, DEI, AI, wars, BRICS, etc.), official measurements, and value system in our markets, societies, and way of living.

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22

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 (May 29, 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 willdevelop a sophisticated forecasting technique bycombining the weighted average method with Linear Programming (LP) model by developing an alternative technique to calculate the weights. We will carry out our analysis by using Microsoft Excel, statistical data analysis tool R and MATHEMATICA. We will demonstrate our model by numerical examples. Dhaka Univ. J. Sci. 71(1): 36-41, 2023 (Jan)

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23

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 (April 30, 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 production, comparing them over two decades. Through rigorous statistical analysis using measures like MAD, MSE, and MAPE. Grey Forecasting emerges as more consistent and accurate. In 2023, there was an increase of approximately 13.86% compared to 2022. In 2024, this figure rose to about 25.08% compared to 2023. Similarly, in 2025, there was an increase of roughly 18.17% compared to 2024. The study's contribution lies in its comprehensive long-term data analysis, offering new insights into Grey Forecasting's application. These findings could significantly impact Philippine agricultural planning and policies, prompting further research to refine forecasting methods. Emphasizing the value of advanced predictive models in agriculture, the study advocates for informed decision-making and resource allocation. Future research should focus on refining these models by incorporating broader datasets and advanced algorithms to improve accuracy and reliability.

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24

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, and multiple prediction algorithms. This combined forecasting system can overcome the inherent disadvantages of the traditional forecasting methods and further improve the prediction performance. To test the effectiveness of the forecasting system, the 10-min and one-hour wind speed sequences from the Sotavento wind farm in Spain were applied for conducting comparison experiments. The results of both the interval forecasting and point forecasting indicated that, in terms of the forecasting capability and stability, the proposed system was better than the compared models. Therefore, because of the minimum prediction error and excellent generalization ability, we consider this forecasting system to be an effective tool to assist smart grid programming.

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25

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 (January 18, 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 berupa sebuah aplikasi Forecasting penjualan berbasis website. mempunyai fitur Pencataatan satuan, data barang, data penjualan dan Forecasting penjualan. Dari hasil pengujian terhadap fitur aplikasi Forecasting dapat berjalan dengan baik. Pengujian pengguna dapat memahami dalam menggunakan fitur pada aplikasi Forecasting penjualan Toko Agung berbasis website. Dari hasil penelitian disimpulkan dapat memudahkan Pemilik untuk mengolah dan meramalkan transaksi barang. Metode Single Exponential Smoothing mengunakan nilai bobot data lampau dan memberi bobot di setiap data untuk mencari beda suatu prioritas nilai data. Metode Single Exponential Smoothing digunakan untuk jangka wakru singkat umunya 1 bulan ke depan yang nilai data naik-turun didaerah Mean tanpa trend atau pola yang konsisten konsisten. Hasil akurasi dengan nilai alpha 0.1 menghasilkan MAPE 4.19 %.

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26

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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27

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

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28

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

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29

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

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30

Clark, Mary E. "Economic Forecasting." Science 246, no. 4926 (October 6, 1989): 10. http://dx.doi.org/10.1126/science.246.4926.10.b.

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31

Lieberman, Bernhardt. "Economic Forecasting." Science 246, no. 4926 (October 6, 1989): 10. http://dx.doi.org/10.1126/science.246.4926.10.a.

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32

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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33

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

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34

Siviero, Stefano, and Daniele Terlizzese. "Macroeconomic Forecasting." Journal of Business Cycle Measurement and Analysis 2007, no. 3 (July 22, 2008): 287–316. http://dx.doi.org/10.1787/jbcma-v2007-art14-en.

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35

Gneiting, Tilmann, and Matthias Katzfuss. "Probabilistic Forecasting." Annual Review of Statistics and Its Application 1, no. 1 (January 3, 2014): 125–51. http://dx.doi.org/10.1146/annurev-statistics-062713-085831.

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36

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

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37

CLARK, M. E. "Economic Forecasting." Science 246, no. 4926 (October 6, 1989): 10. http://dx.doi.org/10.1126/science.246.4926.10-a.

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38

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

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39

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

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40

Pielke Jr., R. A. "Hurricane Forecasting." Science 284, no. 5417 (May 14, 1999): 1123c—1123. http://dx.doi.org/10.1126/science.284.5417.1123c.

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41

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

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42

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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43

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

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44

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

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45

Swanson, David A. "Demographic Forecasting." Contemporary Sociology: A Journal of Reviews 38, no. 4 (July 2009): 369–70. http://dx.doi.org/10.1177/009430610903800445.

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46

Tryfos, Peter, and Russell Blackmore. "Forecasting Records." Journal of the American Statistical Association 80, no. 389 (March 1985): 46–50. http://dx.doi.org/10.1080/01621459.1985.10477128.

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47

Stern, Victoria. "Forecasting Malaria." Scientific American 19, no. 1 (March 2009): 10. http://dx.doi.org/10.1038/scientificamericanearth0309-10b.

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48

Cassidy, Tracy Diane. "Colour forecasting." Textile Progress 51, no. 1 (January 2, 2019): 1–137. http://dx.doi.org/10.1080/00405167.2019.1659564.

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49

Figlewski, Stephen. "Forecasting Volatility." Financial Markets, Institutions and Instruments 6, no. 1 (February 1997): 1–88. http://dx.doi.org/10.1111/1468-0416.00009.

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SAMSON, KURT. "Forecasting Seizures." Neurology Now 3, no. 3 (May 2007): 44–45. http://dx.doi.org/10.1097/01.nnn.0000279084.22572.3d.

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