Abstract
Global temperature change is an important indicator of climate change. Climate time series data are characterized by trend, seasonal/cyclical as well as irregular components. Adequately modeling these components cannot be overemphasized. In this paper, we have proposed an approach of modeling temperature data using semiparametric additive generalized linear model. We have derived a penalized maximum likelihood estimation of the additive component of the semiparametric generalized linear models, that is, of regression coefficients and smooth functions. A statistical modeling with real time series data set was conducted on temperature data. The study has provided indications on the gain of using semiparametric modeling in situations where a signal component can be additively decomposed in to trend, cyclical and irregular components. Thus, we recommend semiparametric additive penalized models as an option to fit time series data sets in modelling the different component with different functions to adequately explain the relation inherent in data.
Highlights
A systems or a signal is defined by a set of entities, named as components that are jointly connected
Global temperature change is an important indicator of climate change, by no means the only one, [3]
The following time series analysis models: moving average method, exponential smoothing method, and exponential smoothing with trend method were used to estimate greenhouse gas emissions. For suitability of their models, they performed a statistical analysis on their results based on mean error, mean absolute error and root mean square values to assess the performance of the formulated models
Summary
A systems or a signal is defined by a set of entities, named as components that are jointly connected. These connections are accountable for defining the various relationships and dependencies among all components. The knowledge of the components and understanding their connections according to [1], is an important way to modeling the system in order to improve decision making, forecasting, controlling system among other things. Its effects are felt locally, but the global distribution of climate response to many global climate changes is reasonably congruent in climate models, suggesting that the global metric measure is useful [4]. Africa experienced extreme weather events and more irregularity in weather patterns, leading to serious outcomes for the people, who depend on land and some water bodies to survive, [5]
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