Using Fuzzy Logic to Predict Transformation Function Models
DOI:
https://doi.org/10.31272/jae.i151.1530Keywords:
Proposed Forecasting Model, Time series data, ImprovementAbstract
This research aims to predict temperatures using transformation function models, which are time-based models used to describe the dynamic relationship between input variables (solar radiation) and output variables (temperature). The model was initially built using the original data and then reconstructed after applying data blurring techniques to reduce noise and improve the quality of the time series. The results showed that the model after blurring performed better than the model using the original data, achieving higher accuracy in short-term predictions. This finding underscores the importance of preprocessing techniques, such as data blurring, in enhancing the efficiency of statistical models and improving forecasting results in climate applications.
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