Local Learning for Iterated Time-Series Prediction
نویسندگان
چکیده
We introduce and discuss a local method to learn one-step-ahead predictors for iterated time series forecasting. For each single one-step-ahead prediction, our method selects among diierent alternatives a local model representation on the basis of a local cross-validation procedure. In the literature , local learning is generally used for function estimation tasks which do not take temporal behaviors into account. Our technique extends this approach to the problem of long-horizon prediction by proposing a local model selection based on an iterated version of the PRESS leave-one-out statistic. In order to show the eeectiveness of our method, we present the results obtained on two time series from the Santa Fe competition and on a time series proposed in a recent international contest.
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