Abstract
This study utilizes advanced computational tools and statistical methods to analyze three-term patterns in temperatures from 1900 to 1991, from 1991 to present, and forecasts for the next ten years. A combination of algorithms, coding, and machine learning techniques are employed to visually represent the behavior of temperature trends, incorporating a 95% confidence interval and smoothing coefficients Alpha, Beta, and Gamma. The data reveals a consistent increase in temperatures over the past 30 years, with a noticeable variation in temperature distribution throughout the calendar year compared to the previous century. Of particular interest are the projected rises in average temperatures for the months of April, June, August, and October, with July expected to experience the highest average temperature. Additionally, a striking observation is the significant difference in average temperatures for the month of January, potentially indicating the exclusion of this month in the temperature’s calendar. Looking ahead, the forecasted data shows a pattern of steadily rising average temperatures, surpassing those recorded in the past three decades. This progression also illustrates a potential merging of the average temperatures for January and December towards that of February, predicting further warming trends and potential ecological impact.
Presenters
Wisam BukaitaAssistant Professor, Math and Computer Science, Lawrence Technological University, Michigan, United States
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Temperature, Forecasting, Global Warming, Data Analysis
