Forecasting with Time Series Models

Abstract

Having selected a model and fitted its parameters to a given times series, the model can then be used to estimate new data of the time series. If such data are estimated for a time period following the final data value XT of the given time series, we speak of a prediction or forecast. The estimation of data lying between given data points is called interpolation. The question now arises as to how a model such as those given in Equations 30.6 or 30.13 could be used to obtain an "optimal" estimate.KeywordsTime SeriesForecast ErrorConditional ExpectationConditional VarianceTime Series ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.