6533b835fe1ef96bd129ee1b

RESEARCH PRODUCT

Prediction and interpolation of time series by state space models

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Artikkeliväitöskirja. Sisältää yhteenveto-osan ja neljä artikkelia. Article dissertation. Contains an introduction part and four articles. A large amount of data collected today is in the form of a time series. In order to make realistic inferences based on time series forecasts, in addition to point predictions, prediction intervals or other measures of uncertainty should be presented. Multiple sources of uncertainty are often ignored due to the complexities involved in accounting them correctly. In this dissertation, some of these problems are reviewed and some new solutions are presented. A state space approach is also advocated for an e cient and exible framework for time series forecasting, which can be used for combining multiple types of traditional time series and other models.