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- W1495283698 abstract "Abstract Preprocessing and forecasting nonstationary time series has always been a difficult task. Depending on the methods and tools used, we extract different features from a process, and these features provide us with some routes for forecasting the evolution of a process. Forecasting has extensively and successfully been done for stationary processes. However, nonstationarity smears many convenient properties of stationarity. For forecasting, the conceptual gap between stationary and nonstationary processes must be bridged. Besides, more robust methods which are not easily influenced by the evolution of nonstationary characteristics should be used. In this article, we first familiarize the readers with the concept of local stationarity as the bridge between stationarity and nonstationarity, and then present both parametric and nonparametric methods for nonstationary process preprocessing. Finally, prediction algorithms based on parametric and nonparametric concepts are introduced." @default.
- W1495283698 created "2016-06-24" @default.
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- W1495283698 date "2011-01-01" @default.
- W1495283698 modified "2023-09-27" @default.
- W1495283698 title "Forecasting NonStationary Processes" @default.
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- W1495283698 doi "https://doi.org/10.1002/9780470400531.eorms0331" @default.
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