Objective: To predict forthcoming monthly sales using Autoregressive Models (ARIMA) in Python.

**Details: **Most of the business units across the industries heavily rely on time-series data to analyze and predict say, the leads/ sales/ stocks/ web traffic/ revenue, etc. to make any strategic business impacts from time to time. Interestingly, time-series models are gold mines of insights when we have serially correlated data points. Let’s look into such a time-stamped sales dataset from Kaggle to understand the key steps involved in the time-series forecasting using Autoregressive (ARIMA) models in Python.

Here we are applying ARIMA models over a transactional sales dataset to predict the monthly sales of an organization with an inbound and outbound variance. In the real world, we need a five-stage plan for time-stamped predictive modeling — namely, Data Pre-processing, Data Evaluation, Model Selection, Model evaluation, and last but not the least forecasting into the future.

#python #time-series-analysis #timeseries #arima #data-science