在今日電子化的時代，隨著時間的演進，各行各業的電子資料庫裡，累積了大量的時間序列資料，如何有系統歸納與分析資料，以萃取出隱藏於其中的重要資訊作為管理者進行決策參考或預測未來之依據，是一件具挑戰且重要的工作，其中時間序列的預測更是決策者於決策過程中不可或缺的重要工作，因而許多有關時間序列預測的相關理論與方法紛紛被提出，如傳統統計時間序列預測法、類神經網路時間序列預測法與模糊時間序列預測法等。然而此大部分的研究多侷限於短期時間序列的預測，對於長期時間序列預測之研究相對地不多，探究其主因在於長期時間序列預測必須以當前的預測值當成輸入值，反覆地進行預測，易導致誤差累積的情況產生，可能導致預測準確度過低的缺失，因此長期時間序列預測是困難且具挑戰的。另一方面，在實務應用上，若能即時預測出每週的股市變動、整個月的電力使用量、或一季的股市變動等，如此之長期時間序列預測，將使決策者更有充裕的時間預做準備。目前已有少數學者對於長期時間序列進行研究，然其研究方法大多使用定量時間序列預測模型進行預測，且所收集的資料往往存在些不確定性，若僅以定量時間序列預測模型進行預測或忽略資料的不確定性，將容易造成誤導的預測。為解決此問題，本研究導入模糊理論的概念與結合模糊時間序列預測法，提供一個以「模糊語意樣式」為基礎的長期時間序列預測模式，以期能提供更適合的長期時間序列預測模型。為強化長期時間序列模型預測的功能性，我們提出結合法則式預測法與向量量化法的整合性模型，使其得以兼具純量長期時間序列預測與向量長期時間序列預測的能力。為驗證本研究模式的效能，我們以兩個資料集進行研究，並透過蒙地卡羅實驗與傳統統計時間序列法(ARIMA)、類神經網路時間序列預測法(BPN)與模糊時間序列預測法(FTS)進行比較，實驗結果驗證本研究所提之模型能有效進行純量與向量長期時間序列預測並且提高預測準確度。

With the fast growth of the temporal data evolving over time, a great many varieties of time series forecasting techniques have been studied for providing precise results efficiently, which include traditional statistics, artificial neural networks and fuzzy methods. However, the forecasting capabilities of most of the aforementioned techniques are limited to short-term time spans in which a single future value is forecasted in one step. Nevertheless, there is an increasing need for long-term forecasting going many time steps in advance. For instance, when forecasting the monthly energy consumption of households, it would be more useful to predict the long-term trend and all monthly values for one season in a single step. Long-term forecasting is a task that is difficult to achieve because information is unavailable for the unknown future time steps. Although there have been long-term forecasting models proposed in the recent years, they mainly deal with numeric historical data. However, in practice uncertainties, such as incompleteness, impreciseness and ambiguousness are likely to be widespread in real-world data, and hinder forecasting accuracy, thus limiting the applicability of these models. To deal with such uncertainties, in this study we propose a fuzzy pattern linguistics based long-term time series forecasting model, which is designed for vector long term time series forecasting by combining the concepts of both fuzzy set theory and time series forecasting technique.
In the proposed method, we incorporate the well-recognized sliding-window scheme to extract features of interest in a time series. Fuzzy c-means (FCM) clustering is then used to handle interval partitioning, in which we take the density and uncertainty of data points into account, and unequal-size intervals are constructed. In this work, the temporal relationships in a fuzzy time series are deterministically extracted and represented as certain transition rules to facilitate the forecasting. In the case when no historical certain rules are available for the unseen time series, the forecasting is enhanced by applying vector quantization (VQ). Finally, due to the uncertainties of the initially assigned membership degrees in FCM clustering, Monte Carlo simulation is utilized to verify the reliability of the proposed model. To validate the effectiveness of the proposed method, we conduct real-world experiments on monthly accumulated rainfall and daily temperature in Taiwan. The results indicate that the proposed method provides better forecasting ability than traditional statistics (ARIMA), artificial neural networks (BPN) and fuzzy methods (Deterministic Fuzzy Time Series).

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