RETRACTED ARTICLE: Time-series data prediction problem analysis through multilayered intuitionistic fuzzy sets

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...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2023-02, Vol.27 (3), p.1663-1671
Main Authors: Dwivedi, Atul Kumar, Kaliyaperumal Subramanian, Umadevi, Kuruvilla, Jinsa, Thomas, Aby, Shanthi, D., Haldorai, Anandakumar
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Intelligence
Control
Decision making
Engineering
Focus
Forecasting
Fuzzy control
Fuzzy logic
Fuzzy sets
Information centers
Mathematical Logic and Foundations
Mechatronics
Methods
Neural networks
Predictions
Robotics
Software
Time series
Weather forecasting
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Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-022-07053-4