In this paper, we propose two nonparametric methods used in the forecasting of functional time-dependent data, namely functional singular spectrum analysis recurrent forecasting and vector forecasting. Both algorithms utilize the results of functional singular spectrum analysis and past observations in order to predict future data points where recurrent forecasting predicts one function at a time and the vector forecasting predicts functional vectors. We compare our forecasting methods to a gold standard algorithm used in the prediction of functional, time-dependent data by way of simulation and real data and we find our techniques do better for periodic stochastic processes.

翻译：在本文中,我们提出了两种在预测基于时间的功能数据中使用的非参数方法,即功能单一频谱分析经常预报和矢量预报。两种算法都利用功能单一频谱分析的结果和以往的观测结果,以预测未来数据点,即经常性预测一次预测一个函数,而矢量预测预测预测预测功能矢量。我们比较我们的预测方法,以模拟和真实数据的方式预测功能、时间依赖数据时使用的黄金标准算法,我们发现我们的技术对定期的随机过程更好。