ARIMA stands for Autoregressive Integrated Moving Average, which is a statistical analysis model used in statistics and econometrics to measure events that happen over a period of time. ARIMA is a generalization of an autoregressive moving average model and is used to better comprehend the data or to forecast upcoming series points. ARIMA combines autoregressive features with those of moving averages, and it predicts a given time series based on its own past values. ARIMA models are applied in some cases where data show evidence of non-stationarity in the sense of mean (but not variance/autocovariance), where an initial differencing step (corresponding to the "integrated" part of the model) can be applied one or more times to eliminate the non-stationarity of the mean function (i.e., the trend) . ARIMA models are becoming a popular tool for data scientists to employ for forecasting future demand, such as sales forecasts, manufacturing plans, or stock prices. The ARIMA model is used as a forecasting tool to predict how something will act in the future based on past performance. ARIMA modeling is generally inadequate for long-term forecasting, such as more than six months ahead, because it uses past data and parameters that are influenced by human thinking.