wherext is the observation at time tet is the noise and ae aqi = the dependency on prior values is denoted by or the order of ar model moving average or ma modelingis again essentially linear regression model that models the impact of noise/error from prior observations to current one the model is denoted as - / jqe - wherem is the series meanei are the noise termsand is the order of the model the ar and ma models were known long before box-jenkin' methodology was presented yet this methodology presented systematic approach to identify and apply these models for forecasting ##note boxjenkinsand reinsel presented this methodology in their book titled time series analysisforecasting and control you are encouraged to go through it for deeper understanding the arima model is logical progression and combination of the two models yet if we combine ar and ma with differenced serieswhat we get is called as arima( , ,qmodel wherep is the order of autoregression is the order of moving average is the order of differencing thusfor stationary time series arima models combine autoregressive and moving average concepts to model the behavior of long running time series and helps in forecasting let' now apply these concepts to model gold price forecasting modeling while describing the datasetwe extracted the gold price information using quandl let' first plot and see how this time series looks the following snippet uses the pandas to plot the same in [ ]gold_df plot(figsize=( )plt show(