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首页> 外文会议>International conference on advanced data mining and applications >An Intelligent Weighted Fuzzy Time Series Model Based on a Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting
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An Intelligent Weighted Fuzzy Time Series Model Based on a Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting

机译：基于正弦余弦自适应人类学习优化算法的智能加权模糊时间序列模型及其在金融市场预测中的应用

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Financial forecasting is an extremely challenging task given the complex, nonlinear nature of financial market systems. To overcome this challenge, we present an intelligent weighted fuzzy time series model for financial forecasting, which uses a sine-cosine adaptive human learning optimization (SCHLO) algorithm to search for the optimal parameters for forecasting. New weighted operators that consider frequency based chronological order and stock volume are analyzed, and SCHLO is integrated to determine the effective intervals and weighting factors. Furthermore, a novel short-term trend repair operation is developed to complement the final forecasting process. Finally, the proposed model is applied to four world major trading markets: the Dow Jones Index (DJI), the German Stock Index (DAX), the Japanese Stock Index (NIKKEI), and Taiwan Stock Index (TAIEX). Experimental results show that our model is consistently more accurate than the state-of-the-art baseline methods. The easy implementation and effective forecasting performance suggest our proposed model could be a favorable market application prospect.
机译：考虑到金融市场系统的复杂性，非线性性，财务预测是一项极具挑战性的任务。为了克服这一挑战，我们提出了一种用于财务预测的智能加权模糊时间序列模型，该模型使用正弦余弦自适应人类学习优化（SCHLO）算法来搜索用于预测的最佳参数。分析了新的加权算子，这些算子考虑了基于频率的时间顺序和库存量，并且集成了SCHLO来确定有效间隔和加权因子。此外，开发了一种新颖的短期趋势修复操作以补充最终的预测过程。最后，该模型被应用于四个世界主要交易市场：道琼斯指数（DJI），德国股票指数（DAX），日本股票指数（NIKKEI）和台湾股票指数（TAIEX）。实验结果表明，我们的模型始终比最新的基线方法更准确。易于实施和有效的预测性能表明我们提出的模型可能是一个良好的市场应用前景。

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