在我們的日常生活中，模糊以及不完全的資料充斥在我們的身邊。因此，近年來，在不確定的環境下模糊時間序列預測漸漸地扮演著越來越重要的角色。各家學者針對改善預測的正確率以及降低運算的成本提出了多種不同的預測模型。一般來說，模糊時間序列預測模型可分為四個階段: (1) 決定以及分割全域 (2) 定義模糊集合並且將資料模糊化 (3) 建構模糊邏輯關係 (4) 預測並且解模糊化產生結果。
大部分過去的研究在建構模糊邏輯關係並且進行預測時，多半採用完全匹配(exact-match)的方式，而此種方式會導致在預測階段忽略了模糊的特性，且會有以下兩個主要的缺點:
(1)假如資料數不足以建構出足夠的模糊邏輯規則，會導致在預測階段找到匹配規則(exact-match rules)的機率降低。
(2)假如在預測階段採用較高階(high-order)的預測模型，那麼過去學者所提出的模型在預測階段會變得較難找到匹配規則。
因此本研究針對了上述所提到的兩項缺點，提出了一個以模糊相似度為基礎的模糊時間序列預測模型，並且能夠有效的提升預測正確率。

In our daily life, vague and incomplete data described as linguistic variables massively exists in various areas. Therefore, fuzzy time series forecasting plays an important role for uncertain situations. Various forecasting models have been proposed with an emphasis on improving forecasting accuracy or reducing computation cost. Generally speaking, the framework of a fuzzy time series forecasting model is constructed of four major steps: (1) determining and partitioning the universe of discourse, (2) defining the fuzzy sets on the universe of discourse and fuzzifying the time series, (3) constructing fuzzy logical relationships existing in the fuzzified time series, and (4) forecasting and defuzzifying of its outputs.
However, most of the researches derive the fuzzy logical relationships in step (3) only using the exact-match IF-THEN rules. Their forecasting models ignore the fuzzy character in forecasting step, and have two shortcomings in the following:
(1) If the data is not enough to generate sufficient fuzzy logical rules, the low rate of rule matching may occur in forecasting process.
(2) If the order of a fuzzy logical relationship is quite high, the previous models will become hardly to find the matching rules in the forecasting process.
Therefore, in this study, we propose the fuzzy similarity-based forecasting model for fuzzy time series to solve the two shortcomings and raise the forecasting accuracy.

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