1. A method of performing data storage optimization comprising: collecting a current set of one or more I/O statistics for a current time period T; determining, using the current set of one or more I/O statistics and one or more models, a predicted set of one or more I/O statistics for a next time period T+1, wherein each of the one or more models is used to model at least a first of the one or more I/O statistics of the predicted set, wherein each of the one or more models is an auto-regressive integrated moving average model characterized by model parameters including P denoting a number of auto-regressive terms, D denoting a number of nonseasonal differences needed for stationarity, and Q denoting a number of lagged forecast errors of prediction, wherein each of the one or more models uses an equation or function including coefficients, and wherein, for at least a first of the one or more models, values of the model parameters and the coefficients are selected in accordance with observed I/O workload characteristics of an application; determining, by a data storage optimizer using the predicted set of one or more I/O statistics, one or more data portions for movement from a current storage tier to a target storage tier; and moving at least one of the data portions from the current storage tier to the target storage tier, and wherein each of the one or more models uses the equation or function including a constant, a first set of one or more terms that are the auto-regressive terms and a second set of one or more terms that are lagged forecast errors of prediction, wherein each of the one or more models uses a first set of one or more coefficients and a second set of one or more coefficients, said first set of coefficients being used with the first set of one or more terms and said second set of coefficients being used with the second set of one or more terms.