Linear filters used in seasonal adjustment (model-based or from the X-11 method) contain unit root factors in the form of differencing operators and seasonal summation operators. The extent to which the various filters (seasonal, seasonal adjustment, trend, and irregular) contain these unit root factors determines whether the filters reproduce or annihilate (i) fixed seasonal effects, and (ii) ...

We assess the usefulness of pre-testing for seasonal roots, based on the HEGY approach, for outof-sample forecasting. We show that if there are shifts in the deterministic seasonal components then the imposition of unit roots can partially robustify sequences of rolling forecasts, yielding improved forecast accuracy. We illustrate with two empirical examples where more accurate forecasts can be...

[1] Southern Hemisphere (SH) climate change has been partly attributed to Antarctic ozone depletion in the literatures. Here we show that the ozone hole has affected not only the long-term climate change but also the interannual variability of SH surface climate. A significant negative correlation is observed between September ozone concentration and the October southern annular mode index, res...

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algori...

The domain of multi level forecast combination is a challenging new domain containing a large potential for forecast improvements. This thesis presents a theoretical and experimental analysis of different types of forecast diversification on forecast error covariances and resulting combined forecast quality. Three types of diversification are used: (a) diversification concerning the level of le...

Many time series exhibit seasonal variations related to the daily, weekly or annual activity. In this paper a new immune inspired univariate method for forecasting time series with multiple seasonal periods is proposed. This method is based on the patterns of time series seasonal sequences: input ones representing sequences preceding the forecast and forecast ones representing the forecasted se...

The relative merits of the TAO/TRITON and PIRATA mooring networks, the VOS XBT network, and the ARGO float network are evaluated through their impact on ocean analyses and seasonal forecast skill. An ocean analysis is performed in which all available data are assimilated. In two additional experiments the moorings and the VOS data sets are withheld from the assimilation. To estimate the impact ...

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. We propose first to reduce the dimensionality by applying functional principal component analysis to the historical observations, and then to use univaria...