Methods for studying time series are AR (autoregressive), MA (moving average), ARMA (autoregressive moving average). For a stationary time series prediction problem, the first thing to consider is to find the best prediction model with it. And the identification of the model and the order of the selection is the key to the selection model. 1. Use iteration to generate 1000 points (the first two points to customize). 2. Take the 800 points in the 1000 points for time series analysis to establish the appropriate model. 3. Use the remaining 200 points to model the forecast and see if it matches and finalize it.

Test.fig
Test.m

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