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Since many climate time series contain a stochastically trending component (36), the nature of the series must be explored before testing for climate change.
β is the attenuation constant of the stationary estimation and takes values in the interval (0, 1), m is the number of periods to be forecasted in the future, l is the seasonal length (by month or year quartile), T is the trending component, S the seasonal adjusting factor and Ŷ n + m is the forecasting value for the following m period.
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Countries with the highest jobless rates: Greece: 27.2% (in January) Spain: 26.2% Portugal: 17.5% Countries with the lowest jobless rates Austria: 4.7% Germany: 5.4% Luxembourg: 5.7% Update As a clarification on the German figure: Eurostat says it uses the trend component for Germany "instead of the more volatile seasonally adjusted data".
On completing the extracting trend component, the trend is removed.
Trend component stands for the general trend of one series.
However, strong drops in the trend component are observed.
Open image in new window Fig. 7 Trend component.
The extraction order is the trend component with periodic component, followed by the random component.
Hence (S_{t}) has a trend component that is not negligible compared to its seasonal component.
However, as the time period increases longer, the trend component becomes nonlinear.
The trend component is dominant during periods of longer than a few days.
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