Prediction with incomplete past and interpolation of missing values
A generalized innovation algorithm is used to solve the problems of prediction of future values based on incomplete past and interpolation of missing values of a stationary time series. The emphasis is on the computational aspects and the proposed method is particularly useful when there are several missing values with arbitrary patterns.
Year of publication: |
1997
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Authors: | Cheng, R. ; Pourahmadi, M. |
Published in: |
Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 33.1997, 4, p. 341-346
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Publisher: |
Elsevier |
Keywords: | Stationary time series Innovation algorithm Autoregressive parameters Variance of errors |
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