• Malde Ritik Vimal
• Shaikh Mohammad Bilal Naseem

Time series models have been the basis for any study of a behavior of process or metrics over a period of time. Machine learning is a subset of artificial intelligence (AI) that provides system the ability to automatically learn and improve from experience. The applications of time series models are manifold including sales forecasting, weather forecasting etc. Goal of this research is to explain the uni-variate forecasting, concept of stationarity, Auto Regressive Integrated Moving Averages model and Seasonal Auto Regressive Integrated Moving Averages model with eXogenous factor. To keep a track of sales of a company is the major key to gain profits in future and to prevent the further losses. As well as for stock holders it is supreme to keep an eye on stocks of the company to prevent their losses and gain significant profit.

Machine Learning, Time series, Uni-variate forecasting, stationarity, Sarimax.

"TIME SERIES ANALYSIS: FORECASTING WITH SARIMAX MODEL AND STATIONARITY CONCEPT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 12, page no.156-161, December-2020, Available :http://www.jetir.org/papers/JETIREJ06034.pdf

"TIME SERIES ANALYSIS: FORECASTING WITH SARIMAX MODEL AND STATIONARITY CONCEPT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 12, page no. pp156-161, December-2020, Available at : http://www.jetir.org/papers/JETIREJ06034.pdf