Time-series data prediction problem analysis through multilayered intuitionistic fuzzy sets.
Soft comput
; : 1-9, 2022 Apr 15.
Article
in English
| MEDLINE | ID: covidwho-2240426
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Soft comput
Year:
2022
Document Type:
Article
Affiliation country:
S00500-022-07053-4
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