An application of fuzzy time series with different universal discourse interval lengths for rice production in India

Articles in Press

Document Type : Research Paper

Authors

1 PG and Research Department of Mathematics, Rajah Serfoji Government College (Autonomous), Affiliated to Bharathidasan University, Thanjavur, India-613005.

2 Research Scholars, PG and Research Department of Mathematics, Rajah Serfoji Government College (Autonomous), Thanjavur-05 Tamilnadu, India.

3 SRM Institute of Science and Technology, Tiruchirappalli, Tamilnadu, India

10.22098/jhs.2025.15725.1041

Abstract

In this paper, we offered a FTS-based tutorial on rice farming in India. The relevant literature is reviewed, which serves as a basis for the main concepts and models based on different forms of FTS forecasts. In an effort to inspire readers to contribute to this field of study, we also highlight the challenges and recent work that aims to fill in some of these knowledge gaps. Finally, time series forecasting is a useful tool for organizing and making decisions. An increasing number of methods, ranging from traditional statistical models to soft computing and artificial intelligence approaches, have been developed to generate increasingly accurate forecasts. PyFTS is an open-source, free Python library created by the Laboratory of Machine Intelligence and Data Science that implements a number of FTS models that have been published in the literature. In order to determine the interval in the fuzzy time series, Chen's method of FTS, comparing numerous values of n (Number of Interval) is used in this paper. We are interested to minimizing the MSE in the forecasting using PyFTS.

Keywords

Main Subjects

References

[1] A.L. Zadeh, Fuzzy sets, Information and control 8(3) (1965), 338–353.
[2] Chen, Shyi-Ming, and Chia-Ching Hsu,A new method to forecast enrollments using fuzzy time series, International Journal of Applied Science and Engineering 2(3) (2004), 234–244.
[3] Chen, Shyi-Ming, and Chao-Dian Chen, TAIEX forecasting based on fuzzy time series and fuzzy variation groups, IEEE Transactions on Fuzzy Systems 19(1), (2010), 1–12.
[4] Wong, Hsien-Lun, Yi-Hsien Tu, and Chi-Chen Wang, Application of fuzzy time series models for forecasting the amount of Taiwan export, Expert Systems with Applications 37(2), (2010),1465–1470.
[5] P. Arumugam, V. Anithakumari,Fuzzy time series method for forecasting Taiwan export data, International Journal of Engineering Trends and Technology, 4(8), (2013), 344–347.
[6] Cheng, Shou-Hsiung, Shyi-Ming Chen, and Wen-Shan Jian, Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures, Information Sciences, 327, (2016), 272–287.
[7] Chen, Shyi-Ming, and Bui Dang Ha Phuong. Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors, Knowledge-Based Systems, 118, (2017), 204–216.
[8] Panigrahi, Sibarama, and Himansu Sekhar Behera, A study on leading machine learning techniques for high order fuzzy time series forecasting, Engineering Applications of Artificial Intelligence 87, (2020), Article 103245. 
[9] Jallal, Mohammed Ali, Aurora Gonzalez-Vidal, Antonio F. Skarmeta, Samira Chabaa, and Abdelouhab Zeroual, A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction,  Applied Energy 268, (2020), Article 114977.
[10] Hadjira, Abdelmounaim, Hicham Salhi, and Fadoua El Hafa, A comparative study between arima model, holt-winters-no seasonal and fuzzy time series for new cases of COVID-19 in Algeria, American Journal of Public Health Research, (2021), 248–256.
[11] Harif, B. Mohamed. A computational method (CM) for prediction based on student improvements and comparisons with different mean practices, Malaya Journal of Matematik 9(01), (2021), 302–304.
[12] AlRassas, Ayman Mutahar, Mohammed AA Al-Qaness, Ahmed A. Ewees, Shaoran Ren, Renyuan Sun, Lin Pan, and Mohamed Abd Elaziz, Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm, Journal of Petroleum Exploration and Production Technology, (2022), 1–13.
[13] B. Mohamed Harif, K. Perarasan. Python Approach on Fuzzy Time Series Arima (1, 1, 1) Model to Analyse Original and Predict Results for online Retail of Fuel Booking Services, Journal of Hyperstructures 13(1), (2024), 142–155.
Journal of Hyperstructures

Articles in Press, Accepted Manuscript
Available Online from 11 June 2025

Files

Share

How to cite

Statistics