##note causal or cross-sectional forecasting/modeling is where the target variable has relationship with one or more predictor variablesexample regression models (see time series forecasting is about forecasting variable(sthat is changing over time both these techniques are grouped under quantitative techniques as mentionedthere are quite handful of techniques availableeach deep topic of research and study for the scope of this section and we will focus upon arima models (from the box-jenkin' methodologyto forecast gold prices before we move ahead and discuss arimalet' look at few key concepts ey concepts stationarityone the key assumptions behind the arima models we will be discussing next stationarity refers to the property where for time series its meanvarianceand autocorrelation are time invariant in other wordsmeanvarianceand autocorrelation do not change with time for instancea time series having an upward (or downwardtrend is clear indicator of non-stationarity because its mean would change with time (see web site visit data example in the previous sectiondifferencingone of the methods of stationarizing series though there can be other transformationsdifferencing is widely used to stabilize the mean of time series we simply compute difference between consecutive observations to obtain differenced series we can then apply different tests to confirm if the resulting series is stationary or not we can also perform second order differencingseasonal differencingand so ondepending on the time series at hand unit root testsstatistical tests that help us understand if given series is stationary or not the augmented dickey fuller test begins with null hypothesis of series being non-stationarywhile kwiatkowski-phillips-schmidt-shin test or kpss has null hypothesis that the series is stationary we then perform regression fit to reject or fail to reject the null hypothesis rima the box-jenkin' methodology consists of wide range of statistical models which are widely used to model time series for forecasting for this sectionwe will be concentrating on one such model called as arima arima stands for auto regressive integrated moving average model sounds pretty complexrightlet' look at the basics and constituents of this model and then build on our understanding to forecast gold prices auto regressive or ar modelinga simple linear regression model where current observation is regressed upon one or more prior observations the model is denoted as - / -