Use Statsmodels ARIMA to predict on India’s weather for 12 months of the year

Time series forecasting is the use of a model to predict future values based on previously observed values.

Statsmodels is a Python library that is used to analyse and make predictions on time series data.

One statsmodels function that can be used to make predictions on time series data is the ARIMA model. The autoregressive integrated moving average (ARIMA) model is a generalisation of an autoregressive moving average (ARMA) model. The autoregressive (AR) part of ARIMA indicates that the evolving variable of interest is regressed on its own lagged (i.e., prior) values. The moving average (MA) part indicates that the regression error is actually a linear combination of error terms whose values occurred contemporaneously and at various times in the past. The I (for “integrated”) indicates that the data values have been replaced with the difference between their values and the previous values (and this differencing process may have been performed more than once). The purpose of each of these features is to make the model fit the data as well as possible.

Non-seasonal ARIMA models are generally denoted ARIMA(p,d,q) where parameters p, d, and q are non-negative integers, p is the order (number of time lags) of the autoregressive model, d is the degree of differencing…