TIME SERIES ANALYSIS AND FORECASTING OF LONG-MEMORY DATA: AN APPLICATION TO CLIMATE CHANGE
Publication Date : 31/10/2024
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Abstract :
This study is designed to develop a hybrid time series model. A statistical model is said to be hybrid if it combines two or more existing models for a better and efficient performance. Our new hybrid model will be used to model time series data such as those generated by climate change and environmental agents. Data generated by climate change and environmental agents are usually not normally distributed hence they are characterised as heavy. Literature showed that quite a number of researchers have studied ARIMAX associated with exogenous covariate (s), using different short-memory frequency data, with little or no strength to capture long memory (high frequency) observations with heavy tailed traits. Having in mind that conventional ARIMAX model has been rarely applied to any of the climate change and environmental agents which are the most cognate agent with associated exogenous variables and are usually characterized by kurtosis, skewness, outliers, long memory (high frequency) and large fluctuation series; this study, therefore, proposes a more robust and sufficient model that would be needed for modeling time series observational data with heavy tailed traits. Keywords: Time, Long-Memory Data, Distributions, Model, Tailed, Traits.
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