how to build an arima model
to begin building an arima model for an investment you download as much of the price data as you can once you ve identified the trends for the data you identify the lowest order of differencing d by observing the autocorrelations if the lag 1 autocorrelation is zero or negative the series is already differenced you may need to difference the series more if the lag 1 is higher than zero next determine the order of regression p and order of moving average q by comparing autocorrelations and partial autocorrelations once you have the information you need you can choose the model you ll use 4pros and cons of arimaarima models have strong points and are good at forecasting based on past circumstances but there are more reasons to be cautious when using arima in stark contrast to investing disclaimers that state past performance is not an indicator of future performance arima models assume that past values have some residual effect on current or future values and use data from the past to forecast future events the following table lists other arima traits that demonstrate good and bad characteristics good for short term forecastingonly needs historical datamodels non stationary datanot built for long term forecastingpoor at predicting turning pointscomputationally expensiveparameters are subjective