Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{190010,
        author = {Dr.R.Sugunthakunthalambigai and Dr.R.Brimapureeswaran and DrM.Radha and Dr.R.Seetha and Dr.U.Arulanandu},
        title = {Fuzzy Time Series–Based Optimization for Aquaculture Production Forecasting in India},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {2714-2719},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190010},
        abstract = {Fuzzy time series (FTS) models are effective forecasting tools for systems characterized by uncertainty and vagueness, as they do not rely on the strict assumptions of classical time series techniques. Aquaculture plays a crucial role in India’s food security, particularly in the southern and eastern regions. Accurate forecasting of fish production is therefore essential for sustainable planning and policy formulation. This study proposes a fuzzy time series–based optimization approach to forecast aquaculture production in India using historical data from 1994–95 to 2016–17. Linguistic variables and fuzzy logical relationship groups are employed to model production trends. Forecasting accuracy is evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE). The results reveal a MAPE value of 3.42%, indicating highly accurate forecasting performance and confirming the suitability of the proposed approach for aquaculture production forecasting.},
        keywords = {Fuzzy time series, Aquaculture forecasting, Linguistic variables, Fuzzy logical relationship groups, Fish production.},
        month = {January},
        }