Weather conditions vary continuously and are influenced by multiple atmospheric parameters, making the analysis and prediction of weather patterns an important area of data-driven research. This paper presents a study of a Weather Forecasting and Climate Analytics system developed using R programming. The system focuses on the analysis of historical weather data containing parameters such as temperature, humidity, rainfall, wind speed, and atmospheric pressure. The methodology includes data preprocessing, exploratory data analysis, visualization, correlation analysis, and time-series analysis to identify important patterns and relationships within the dataset. The Autoregressive Integrated Moving Average (ARIMA) model is applied to forecast future weather conditions based on historical observations and temporal patterns. In addition to forecasting, the proposed system focuses on climate analytics by examining monthly and seasonal variations, rainfall patterns, temperature trends, humidity behavior, and weather differences across different locations. Graphical representations are used to make these patterns easier to interpret and understand. The proposed approach combines statistical analysis, visualization, and ARIMA-based forecasting within the R environment to provide an integrated framework for understanding historical weather behavior and generating data-driven predictions. The study also considers limitations related to dataset quality, missing observations, extreme weather events, and the ability of traditional time-series models to capture complex atmospheric relationships. The approach can provide useful analytical support for applications such as environmental monitoring, agriculture, and weather-related planning.

Keywords— Weather Forecasting; Climate Analytics; R Programming; Time-Series Analysis; ARIMA