This is a continuation of the Time Series Analysis posts. Here, I will do a deep dive into a time series model called ARIMA, an important smoothing technique used commonly throughout the data science field.
If you have not read part 1 of the series on the general overview of time series, feel free to do so!
ARIMA stands for Autoregressive Integrated Moving Average. These models aim to describe the correlations in the data with each other. You can use these correlations to predict future values based on past observations and forecast errors. Below are ARIMA terms and definitions you must understand to use ARIMA!
1) Stationarity:One of the most important concepts in time series analysis is stationarity. Stationarity occurs when a shift in time doesn