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- The multivariable fuzzy time series Heuristic model is the easiest method to follow. 而在方法操作上，以多变量模糊时间数列引导式模式最爲简易。
- Among them,the enrollment forecasting at the university of Alabama is the best model of fuzzy time series. 其中 ;对Alabama校入学人数的预测是应用模糊时间系统最好的例子 .
- This study compares the forecasting methods of ARIMA time series and fuzzy time series by Two-factor models, Heuristic models, and Markov models based on the amount of Taiwan export. 摘要本研究目的是针对传统时间数列模式与模糊时间数列之二因子模式、引导式模式及马可夫模式预测方法在应用上之比较，并以台湾出口金额之预测为例。
- Analysis and Application of Multivariable Fuzzy Time Series 多变量模糊时间数列之分析及应用
- Fuzzy Time Series Analysis Based on Structure Element and Its Application 基于结构元的时间序列分析与应用
- Keywords:Back-Propagation neural network;prediction;parameter adaptable BP alg_ orithm;stock prices;fuzzy time series 关键词:前馈神经网络;预测;参数自适应BP算法;股票价格
- fuzzy time series 模糊时间序列
- fuzzy time series (FTS) 模糊时间序列
- This is called a deseasonalizing time series. 这叫调和时间数列。
- Time Series Analysis Hamilton J.D. 时间序列分析。
- Predicts the future values for a time series. 预测一个时序的未来值。
- Results A new mode of time series is established. 结果建立了一个新的时间序列模型。
- A new time series prediction method was put forward, based on fuzzy least square support vector machine (FLS-SVM). 摘要提出了一种基于隶属度模糊最小二乘支持向量机(FLS-SVM)的时间序列预测新方法。
- Fuzzy neural network with incremental learning ability (FNN-IL) was proposed to predict financial time series. 提出使用一种具有增量学习能力的模糊神经网络(FNN-IL)应用于金融时间序列的预测。
- Tab to display the tree view of the time series model. 选项卡可以显示时序模型的树视图。
- Returns predicted future or historical values for time series data. 返回时序数据的将来或历史的预测值。
- Time series data set comes with a temporal ordering. 时间序列数据集伴随着一个时间上的排序。
- This paper studies algebraic modeling of discrete time series. 摘要主要研究离散时间序列上的代数模型。
- A prediction method for non-stationary time series was proposed in association with multi-resolution of wavelet analysis and interpretability of fuzzy rules. 摘要结合小波分析的多分辨特性和模糊规则的可解释性，提出了一种非平稳时间序列预测方法。
- After the time series fuzzy fractal processing of the mine gas emission quantity, the non linear relations of the influence factors were combined with BP neural network. 通过对矿井瓦斯涌出量时间序列的模糊分形处理,用BP神经网络对影响因素间的非线性关系进行拟合。
