Time Series 4
نویسنده
چکیده
How should you model a process that has drift? ARMA models are intrinsically “stationary,” that is, they are mean-reverting: when the value of xt is above its long-term mean, its next motions will likely be in a downward direction; when it is below its long-term mean the next motion will likely be up. These models also have decaying autocorrelation: the present value is completely forgotten if you go long enough into the future. In practice a lot of models have some sort of steady drift. For example, if you look at the daily volume of some stock, there is likely to be a substantial upward trend as overall trading volumes have increased. Now we discuss three possible ways that such drift can be included in a model.
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