Live demo event: How to automatically best fit R model and implement ARIMA, AR(p,q) ARMA autoregressive simulation against a time series
This is pretty well our first compete end to end R script strategy that includes real world market data capture, parallelizing processes with a complete model with plotting. As ARIMA is one of the most popular forecasting model types out there, I look at the various types of parameters used in auto-regression with p (AR)and q (moving average) parameters. I also show a custom function that can be used to auto fit this including parallelizing. It is important when to test for a stationary time series and when to differentiate it. I also show which R package to use to run an ARIMA processed simulation with proper prediction. End result plots are also generated.
Quant analytics: Youtube video demo of how to find Best Fit parameters of ARMA or AR autoregressive for your ARIMA model forecast in R
Now you don’t need to manually figure your ARMA set of parameters like AR(1,0) or AR(1,1) or AR(2,2). This can automatically done on the fly by this intelligent R script I found and modified. I also include a private video code walkthrough on how this is done
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