• Weather Forecasting is the process of making predictions of the future, based on past and present data of the weather. • We used ARIMA model(Auto Regressive Integrated Moving Average) to analyze and predict the time-series data and we shall also perform rigorous exploratory data analysis and visualizations on the dataset. • Feature Engineering – selecting required attributes. • Data cleaning – renaming attributes and filling missing data. • Check rolling mean and standard deviation (graph must not vary too much for stationarity). • Perform Augmented Dickey–Fuller test (to check for stationarity) • plotting PACF(partial auto correlation function) and ACF(auto correlation function) to find p and q values of ARIMA model. • Fitting and forecasting the model for temperature data. • This could be also be used other types of time series data such as stock prices, market price variations, etc.