Six time series related to atmospheric phenomena are used as inputs for experiments offorecasting with singular spectrum analysis (SSA). Existing methods for SSA parametersselection are compared throughout their forecasting accuracy relatively to an optimal aposteriori selection and to a naive forecasting methods. The comparison shows that awidespread practice of selecting longer windows leads often to poorer predictions. It alsoconfirms that the choices of the window length and of the grouping are essential. Withthe mean error of rainfall forecasting below 1.5%, SSA appears as a viable alternative forhorizons beyond two weeks.

翻译：以六种与大气现象相关的时间序列作为奇异谱分析（SSA）预测实验的输入数据。本文比较了现有SSA参数选择方法相对于最优后验选择和朴素预测方法的预测精度。比较结果表明，广泛采用的较长窗口选择策略往往会导致较差的预测效果，同时验证了窗口长度和分组选择的关键作用。在降雨预测平均误差低于1.5%的情况下，SSA可作为两周以上预测期限的有效替代方案。