In this study, it has attempted both univariate and multivariate approaches of time series analysis. This study has conducted at Slyhet division in Bangladesh. Firstly, we discussed the methodologies that are used in this study. Secondly, we have shown the univariate time series (monthly maximum temperature, monthly minimum temperature, monthly maximum humidity, monthly minimum humidity, monthly average cloud amount, monthly average rainfall, monthly average sea level pressure in Sylhet) graphically to study their descriptive statistics. Thirdly, we have fitted SARIMA model for each of the univariate time series. The fitted models for monthly maximum temperature, monthly minimum temperature, monthly maximum humidity, monthly minimum humidity, monthly average cloud amount, monthly average rainfall and monthly average sea level pressure are ARIMA (1,0,0)×(2,0,0)12, ARIMA (2,0,0)×(2,0,0)12, ARIMA (3,1,0)×(2,0,0)12, ARIMA (0,0,0)×(2,0,0)12, ARIMA (0,0,0)×(2,0,0)12, ARIMA (1,0,0)×(2,0,0)12 and (1,0,0)×(2,0,0)12 respectively. We have constructed the VAR (2) model which includes seven equations for the seven variables. These all models have successfully passed the diagnostic stage.