Nonparametric Forecasting in Time Series - A Comparative Study
نویسندگان
چکیده
منابع مشابه
Nonparametric Forecasting in Time Series - A Comparative Study
The problem of predicting a future value of a time series is considered in this paper. If the series follows a stationary Markov process, this can be done by nonparametric estimation of the autoregression function. Two forecasting algorithms are introduced. They only differ in the nonparametric kernel-type estimator used: the Nadaraya-Watson estimator and the local linear estimator. There are t...
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In this research, monthly rainfall of Shiraz synoptic station from March 1971 to February 2016 was studied using different time series models by ITSM Software. Results showed that the ARMA (1,12) model based on Hannan-Rissanen method was the best model which fitted to the data. Then, to assess the verification and accuracy of the model, the monthly rainfall for 60 months (from March 2011 to Feb...
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We derive generalization error bounds for traditional time-series forecasting models. Our results hold for many standard forecasting tools including autoregressive models, moving average models, and, more generally, linear state-space models. These non-asymptotic bounds need only weak assumptions on the data-generating process, yet allow forecasters to select among competing models and to guara...
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Summery Various forecasting methods have been developed on the basis of fuzzy time series data, but accuracy has been matter of concern in these forecasts. As in fuzzy time series methods forecasted values depend to some degree on our interpretation of the output of the forecasting model thus different interpretation may lead to different results, this makes the process quite subjective. An obj...
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ژورنال
عنوان ژورنال: Communications in Statistics - Simulation and Computation
سال: 2007
ISSN: 0361-0918
DOI: 10.1080/03610910601158377