Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.
In existing fuzzy time series forecasting models, the accuracy of forecasting excessively relies on priori knowledge and output cannot effectively forecast multi values. The forecasting accuracy reduces drastically when time series data deviate from experience boundary in most models. The generalisation performance is insufficient. To overcome defects of traditional methods, this study proposed a long-term intuitionistic fuzzy time series (IFTS) forecasting model based on vector quantisation and curve similarity measure. In preprocessing of the proposed model, FTS theory is extended to long-term IFTS forecasting scope, the raw historical data are quantised vectors and optimum clustering centroids are searched by intuitionistic fuzzy c-means clustering algorithm. Curve similarity measure algorithm is proposed in procedure of forecasting, which avoids influence of mutation points and overcomes limitation of priori information. Euclidean distance is replaced by Fréchet distance, it is appropriate for such directed time series in vector matching. The proposed model and relevant models are implemented on three different datasets, a synthetic dataset, the monthly total retail sale of social consumer goods and daily mean temperature dataset. The forecasting results, index mean square error and average forecasting error rate indicate that our model performance better in different time series patterns than others.
To solve the problem of low accuracy and high false-alarm rate of existing intrusion detection models for multiple classifications of intrusion behaviors, a network intrusion detection model incorporating convolutional neural network and bidirectional gated recurrent unit is proposed. To solve the problems of many dimensions of features and imbalance of positive and negative samples in the original traffic data, sampling processing is performed with the help of a hybrid sampling algorithm combining ADASYN and RENN, and feature selection is performed by combining random forest algorithm and Pearson correlation analysis; after that, spatial features are extracted by the convolutional neural network, and further features are extracted by incorporating average pooling and max pooling, and then BiGRU is used to extracts long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, the Softmax function is used for classification. In this paper, the proposed model is evaluated on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 data sets with an accuracy of 85.55%, 99.81%, and 99.70%, which is 1.25%, 0.59%, and 0.27% better than the same type model of CNN-GRU.
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