| Summary: | 碩士 === 國立政治大學 === 統計學研究所 === 82 === Representations of dynamic data are always different as the
time interval or measuring tool change. We call these
characteristics of uncertainty fuzziness. But traditional
time series use crisp observations to record a fuzzy dynamic
process. To completely represent, we consider fuzzy time
series replacing the crisp numbers with fuzzy sets and
preserve original fuzziness. In this paper, the fuzzy
autoregressive model (FAR model) of fuzzy time series is
studied and used to forecast the Central government
expenditure and exchange rates, respectively. The
modeling process is according to Box- Jenkins'' (1970)
method of ARMA model and merged with the fuzzy set theory
proposed by Zadeh (1965). Reasonable human judgements and
ways of thinking are taken into consideration throughout the
modeling process to make the FAR model more elastic and
appropriate for forecasting. Unlike certain incorrectly
identified models which lead to inaccurate forecasts, the
FAR model can be widely applied due to its not having any
assumptions on the original time series (e.g., linearity
and stationarity). Finally, the performances of the FAR
model to Central government expenditure and exchange rates are
compared with that of the traditional ARMA model.
Additionally, some properties about fuzzy time series, e.g.,
fuzzy trend and fuzzy stationary, have not been studied in
the literature, and we propose definitions and new
opinions.
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