2020/11/26 by Jordan Trinka, Trinka, Jordan, Hossein Haghbin +3
Mathematics · #46N30 #60G35 #62M10 #62M20 #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical and numerical algorithms
paper · pdf · doi:10.48550/arxiv.2011.13077
openalex publication_date 2020/11/26 · openalex created_date 2023/10/18 · openalex updated_date 2026/07/28
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 makes predictions using 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.