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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Pattern discovery of fuzzy time series for financial prediction
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Pattern discovery of fuzzy time series for financial prediction

机译：用于财务预测的模糊时间序列的模式发现

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A fuzzy time series data representation method based on the Japanese candlestick theory is proposed and used in assisting financial prediction. The Japanese candlestick theory is an empirical model of investment decision. The theory assumes that the candlestick patterns reflect the psychology of the market, and the investors can make their investment decision based on the identified candlestick patterns. We model the imprecise and vague candlestick patterns with fuzzy linguistic variables and transfer the financial time series data to fuzzy candlestick patterns for pattern recognition. A fuzzy candlestick pattern can bridge the gap between the investors and the system designer because it is visual, computable, and modifiable. The investors are not only able to understand the prediction process, but also to improve the efficiency of prediction results. The proposed approach is applied to financial time series forecasting problem for demonstration. By the prototype system which has been established, the investment expertise can be stored in the knowledge base, and the fuzzy candlestick pattern can also be identified automatically from a large amount of the financial trading data.
机译：提出了一种基于日本烛台理论的模糊时间序列数据表示方法，并将其用于辅助财务预测。日本烛台理论是投资决策的经验模型。该理论假设烛台模式反映了市场心理，并且投资者可以根据所识别的烛台模式做出投资决策。我们使用模糊语言变量对不精确和模糊的烛台模式进行建模，并将财务时间序列数据传输到模糊烛台模式以进行模式识别。模糊的烛台模式可以弥合投资者与系统设计者之间的鸿沟，因为它是可视的，可计算的和可修改的。投资者不仅能够理解预测过程，而且能够提高预测结果的效率。将该方法应用于金融时间序列预测问题进行论证。通过已建立的原型系统，可以将投资专门知识存储在知识库中，并且还可以从大量金融交易数据中自动识别模糊烛台模式。

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