Climatic Time Series Forecasting by using the Hybrid TF-SMO Model
DOI:
https://doi.org/10.62933/mk1bpd71Keywords:
Time series forecasting , TF-SMO model, Hybrid TF-SMO , Maximum , minimum and evaporation temperatures , Spider Monkey Optimization (SMO)Abstract
Forecasting is considered an estimation of future values based on past data, and its importance lies in future planning. Necessity often calls for finding a model that logically describes the dynamic relationships connecting a single output series to one or more input series. In this research, daily time series data for several years of maximum and minimum temperatures were used as input variables, and evaporation as a response variable for the Baghdad governorate during the period. (2012-2022). Environmental and climatic data often suffer from problems such as heterogeneity and unreliability, which are results of the non-linearity of that type of data. It is possible to use the Transfer function model, denoted by the symbol TF, to model the causal relationship between the output variable and one or more input variables after synchronizing the data temporally to achieve its homogeneity. The aim of this research is to improve the forecasting of time series data, and one of the proposed methods for this is the hybrid model combining the TF model and the Spider Monkey Optimization (SMO) Algorithm, referred to as the TF-SMO hybrid model. The outputs of the TF model are utilized as inputs in the spider monkey algorithm to integrate linear and nonlinear hybrid effects for data processing, modeling, and improving forecasting accuracy. To improve modeling and forecasting results, the (SMO) Algorithm was used as a method for optimizing the modeling of non-linear data patterns and enhancing forecasting. The hybrid model TF-SMO was proposed, which is a hybrid model of the SMO algorithm as an optimization method with the TF model. As for the issue of data heterogeneity, the Time Stratified (TS) method was used to temporally stratify the data into hot and cold seasons. Where the data for the hot and cold seasons for maximum and minimum temperatures and evaporation were divided into two groups: the first group for the training period and the second group for the testing period, approximately 65% for training and approximately 35% for testing .In this research ,data similar to real data was generated with the same sample size for the hot and cold seasons for the transfer function. The results indicate that the hybrid model achieves better accuracy compared to the TF model. Therefore, it is possible to use the TF-SMO hybrid model structure for similar accuracy in forecasting climatic time series data.
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