隨著巨量資訊時代的來臨，資料量不斷地隨著時間暴增，也發展出越來越多的方法來處理資料流，時間序列分類至今已發展數年了，隨著各領域漸漸的重視，時間序列方法逐漸成熟但如何有效地從時間序列資料中挖掘出有用的資訊或提升預測精確度就變得越來越重要了，然而傳統的時間序列並無法有效的處理語意不清的資料，為了要在不確定環境下處理語意資料，本研究提出了LDA (Latent Dirichlet Allocation ) 模型結合模糊時間序列的方法，目的是為了原本模糊化的動作有了統計方法的依據。並且在當今資料爆炸的大數據時代除了精確也講求快速、及時，所以在資料處理方面加入了符號近似法 SAX (Symbolic Aggregate approXimation)特徵擷取方法，可以讓原先高維度的資料對應到低維度的資料空間又可以保留資料特徵，以方便後續的分析應用，這個步驟使得模型訓練方面可以更快速有效的處理大量資料集。
在此研究中主要目標希望能找到一個能夠兼顧精確性及高效率的處理資料，有鑑於LDA模型成功地應用在巨量文件檔的資訊分析上，且具有探勘時間序列資料主題的能力，因此我們將擴展LDA的機率分群方法到模糊時間序列預測上，，提出一個基於機率分群模型之模糊時間序列預測方法，進而提供不同領域對於模糊時間序列的不同需求，並且改善一般傳統模糊時間序列的預測模型。

With the big data age is coming. There is important that how to obtain a useful information from big data and get a good accuracy effectively. Time series analysis has been studied to solve this problem for years. However traditional time series model deal inefficient with fuzzy or incomplete data. This study purpose combining LDA (Latent Dirichlet Allocation ) with fuzzy time series model for solving uncertainty data well. Aspect of data preprocess this study introduces SAX (Symbolic Aggregate approXimation) to extract the feature of data. It can reduce dimensions and keep the data constant. This preprocess makes the model to handle huge dataset efficiently and analysis conveniently.
The purpose of this study wants to develop a model can do data mining with high accuracy and efficiency. In view of LDA model applied to text mining successful, this study want to extend prediction function of model of fuzzy time series, provides a decision model for all fields and improve the traditional time series model.

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