時間序列分類至今已發展約十年，並且在資料探勘領域有相當多的研究方法能夠明顯提升其分類準確度。而近年來資訊科技的演進，使得資料的儲存型態有所改變，大量的資料持續被蒐集、儲存，資料分析者無法一次性地將所有數據儲存於單一記憶體中，而是改採序列的形式將資料點依循時間單位輸入，此種資料處理方式稱為資料流(streaming data)。如今資料流被應用的領域甚廣，例如財務應用、網路監控、資訊安全、通訊管理與製造流程等皆為資料流經常被使用之範圍，其優勢為面臨未知時間長度的大量資料時，仍能有效處理數據並進行分析。
然而資料流輸入快速、大量、時變性(time-varying)與不可預測等特性也造成新的資料處理問題衍生：傳統的資料庫管理系統並無法有效針對上述的資料流特性進行處理(Babcock, Babu, Datar, Motwani, & Widom, 2002)。故為了解決有別於傳統模式的數據分析，我們使用線上學習(on-line learning)對資料進行即時性的處理，包含以動態調整機制進行模糊規則的更新、新增與刪除，使預測模型能更適用於資料流特性的模糊時間序列。
本研究將聚焦於過去較少學者深入探究的領域──將資料流架構加入模糊時間序列。我們以遞迴式密度更新方法持續更新訓練規則庫，同時新增規則或修改規則；進行動態調整之目的在於使訓練規則庫使用率提升、刪去不需要的規則，並且有效改善模糊時間序列預測模式的準確度。其次本研究參考Millán-Giraldo, Sánchez, and Traver (2011)學者提出的預測策略，針對資料傳輸過程中，因人為因素使資料產生部分遺漏之情境提出修正預測模式的方法，以兩種資料輸入策略進行預測，以克服資料流傳輸可能發生的資料延遲情況。

Time series classification has been studied for over a decade and is now widely used in the sphere of data mining to increase the forecasting accuracy. In recent years, the evolution of information technology has caused a change in the data-storage approach. As volume data is collected and stored continuously and rapidly, a data analyzer is not able to efficiently retrieve the information from it over time. Thus a new data-processing approach called ‘streaming’ was proposed, which entails inputting data elements as sequences. The advantage of streaming data is that data points can still be used to forecast future values while the total length is unknown.
Streaming data is diverse, continuous, rapid and time-varying, thus it is not compatible with the conventionally stored data model. To construct a novel approach that differs from a traditional forecasting model, we used on-line learning to process data instantly. We used a dynamic-adjusting mechanism to detect when to add a rule, update a rule or delete a rule. With these steps, we can make our fuzzy time-series forecasting model conform with streaming data well.
In this research, we focused on the combination of streaming data and fuzzy time series. The recursive density updating algorithm is used in our model to decide the rule-updated or rule-added timing. The purpose of using a dynamic-adjusting mechanism is to raise the rule-usage ratio, and to remove redundant rules. In addition, it is also our goal to improve the forecasting accuracy of our model. In doing so, we refer to the on-line learning strategies of Millán-Giraldo, Sánchez, and Traver (2011), who proposed classifying the incoming data with missing attributes. We used two strategies to simulate how to forecast value when parts of the data are delayed.

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