Deterministic vector long-term forecasting for fuzzy time series

Sheng Tun Li, Shu Ching Kuo, Yi Chung Cheng, Chih Chuan Chen

研究成果: Article同行評審

45 引文 斯高帕斯(Scopus)


In the last decade, fuzzy time series have received more attention due their ability to deal with the vagueness and incompleteness inherent in time series data. Although various improvements, such as high-order models, have been developed to enhance the forecasting performance of fuzzy time series, their forecasting capability is mostly limited to short-term time spans and the forecasting of a single future value in one step. This paper presents a new method to overcome this shortcoming, called deterministic vector long-term forecasting (DVL). The proposed method, built on the basis of our previous deterministic forecasting method that does not require the overhead of determining the order number, as in other high-order models, utilizes a vector quantization technique to support forecasting if there are no matching historical patterns, which is usually the case with long-term forecasting. The vector forecasting method is further realized by seamlessly integrating it with the sliding window scheme. Finally, the forecasting effectiveness and stability of DVL are validated and compared by performing Monte Carlo simulations on real-world data sets.

頁(從 - 到)1852-1870
期刊Fuzzy Sets and Systems
出版狀態Published - 2010 7月 1

All Science Journal Classification (ASJC) codes

  • 邏輯
  • 人工智慧


深入研究「Deterministic vector long-term forecasting for fuzzy time series」主題。共同形成了獨特的指紋。