p: The number of lag observations included in the model, also called the lag order.
d: The number of times that the raw observations are differenced, also called the degree of differencing.
q: The size of the moving average window, also called the order of moving average.
AR: Autoregression. A model that uses the dependent relationship between an observation and some number of lagged observations.
I: Integrated. The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary.
MA: Moving Average. A model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.
Title
How to Create an ARIMA Model for Time Series Forecasting in Python
URL
https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/
Summary
explains and shows how to create ARIMA model
Key Points
p: The number of lag observations included in the model, also called the lag order. d: The number of times that the raw observations are differenced, also called the degree of differencing. q: The size of the moving average window, also called the order of moving average.
AR: Autoregression. A model that uses the dependent relationship between an observation and some number of lagged observations. I: Integrated. The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary. MA: Moving Average. A model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.
Citation
Brownlee, Jason. “How to Create an Arima Model for Time Series Forecasting in Python.” Machine Learning Mastery, 9 Dec. 2020, https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/.
Repo link