Nonparametric time series forecasting with dynamic updating
In conclusion, strengths and weaknesses of each technique are discussed.
We estimate a regression of HS on a constant, SP, and the lag of HS, with an AR(1) to correct for residual serial correlation, using data for the period 1959M01–1990M01, and then use the model to forecast housing starts under a variety of settings.
But if a hypothesis is extremely unlikely a priori, one should also reject it, even if the evidence does appear to match up.
For example, if one does not know whether the newborn baby next door is a boy or a girl, the color of decorations on the crib in front of the door may support the hypothesis of one gender or the other; but if in front of that door, instead of the crib, a dog kennel is found, the posterior probability that the family next door gave birth to a dog remains small in spite of the "evidence", since one's prior belief in such a hypothesis was already extremely small.
Abstract We present a nonparametric method to forecast a seasonal univariate time series, and propose four dynamic updating methods to improve point forecast accuracy.
Our methods consider a seasonal univariate time series as a functional time series.