| Autor: |
Dwivedi, Atul Kumar, Kaliyaperumal Subramanian, Umadevi, Kuruvilla, Jinsa, Thomas, Aby, Shanthi, D., Haldorai, Anandakumar |
| Předmět: |
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| Zdroj: |
Soft Computing - A Fusion of Foundations, Methodologies & Applications; Feb2023, Vol. 27 Issue 3, p1663-1671, 9p |
| Abstrakt: |
For several years, time-series prediction seems to have been a popular research topic. Sales plans, ECG forecasts, meteorological circumstances, and even COVID-19 spreading projections are among its uses. These implementations have inspired several scientists to develop an optimum forecasting method; however, the modeling method varies as the implementation domain evolves. Telemetry data prediction is an important component of networking and information center control software. As a generalization of such a fuzzy system, the concept of an intuitionistic fuzzified set was created, which has proven to become a highly valuable tool in dealing with indeterminacy (hesitation) as in-network. Indeterminacy is frequently overlooked in applying fuzzified time-series prediction for no obvious cause. We introduce the concept of intuitionistic fuzzified time series within a current study to deal with non-determinism with time-series prediction. Also, it seems to be an intuitionistic fuzzified time-series prediction framework. Using time-series information, the suggested intuitionistic fuzzified time-series predicting approach employs intuitionistic fuzzified logical relationships. The suggested method's effectiveness is tested using two-time sequence data sets. By contrasting the predicted result with some other intuitionistic timing series predicting techniques utilizing root-mean-square inaccuracy and averaged predicting errors, the usefulness of the suggested intuitionistic fuzzified time-series predicting approach is demonstrated. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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