Ifedaniel/Analyzing-and-Forecasting-Temperature-Variations-with-the-ARIMA-Model

The ARIMA (Autoregressive Integrated Moving Average) model is used in this study to predict and anticipate average temperature changes in five countries: the United Kingdom, the United States, Brazil, Kenya, and India. # Analyzing-and-Forecasting-Temperature-Variations-with-the-ARIMA-Model The ARIMA (Autoregressive Integrated Moving Average) model is used in this study to predict and anticipate average temperature changes in five countries: the United Kingdom, the United States, Brazil, Kenya, and India. INTRODUCTION ------- This study gives a detailed examination and proves the ARIMA model's effectiveness in forecasting temperature trends across these disparate geographical regions. The study's major goal is to demonstrate the ARIMA model's relevance and effectiveness in predicting climatic trends, particularly in the context of average temperature measurements, in the aforementioned nations. # RESULT AND DISCUSSION ------- A. Exploratory Data Analysis (EDA) ------- The first stage of this research entailed completing an Exploratory Data Analysis (EDA), a vital step in comprehensive data analytics, to get a better knowledge of the dataset and inform subsequent decision-making. The dataset was geographically restricted to temperature data from five nations from 1900 to 2015: the United Kingdom, the United States, Brazil, Kenya, and India. During this procedure, we discovered four missing values in the filtered dataset, which we then eliminated. Because of its minor influence, this exclusion was judged insignificant to the overall analytical results. B. Model Application ------- The ARIMA model is technically represented as ARIMA(p, d, q), where 'p' and 'q' are non-negative integers denoting the order of the autoregressive and moving average components, respectively. The parameter 'd' represents the model's needed order of differencing, which is an important component that tackles the stationarity of the time series data [6]. This notation captures the ARIMA model's structural features, reflecting its underlying mathematical and statistical foundation. C. Result ------ Using techniques such as Autocorrelation, Differencing, and Partial Autocorrelation, an ARIMA model configura …