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Flow field forecasting for univariate time series
Author(s) -
Frey Micheal R.,
Caudle Kyle A.
Publication year - 2013
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11191
Subject(s) - univariate , series (stratigraphy) , computer science , time series , field (mathematics) , data mining , econometrics , machine learning , multivariate statistics , mathematics , geology , paleontology , pure mathematics
A statistical learning methodology, called ow eld forecasting, is presented for predicting the future of a univariate time series. Flow eld forecasting draws information from an interpolated ow eld of the observed time series to incrementally build a forecast. The time series need not have uniformly spaced observations. Included in the presentation are measures of assessment, a procedure for forecast updating as new data arrive and a performance comparison of ow eld forecasting with other major forecasting techniques. © 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013