Network traffic forecasting model based on long-term intuitionistic fuzzy time series

Abstract

In this paper, a network traffic forecasting model based on long-term intuitionistic fuzzy time series (LT-IFTS) is proposed. It describes the fuzziness and uncertainty of network flow and improves the traffic forecasting performance. The multi-input multi-output (MIMO) intuitionistic fuzzy time series forecasting model, namely, (p−q) IFTS is defined. An intuitionistic fuzzy time series vectors clustering algorithm based on vector variation pattern is given. The cluster centroid in the proposed model is quite different from the traditional method. As a kind of typical time series data, the network flow forecasting system is constructed particularly. Characteristic intuitionistic fuzzy is a practical method to manage the fuzziness and uncertainty of network traffic data. The network traffic data is intuitionistic fuzzified and vector quantized. The time series vectors are gathered based on the improved intuitionistic fuzzy c-means clustering and matched with centroids by coordinate translation. Compared with other traditional forecasting models, the improved FCM clustering algorithm increases discrimination of time series segments. In addition, the long-term scheme improves forecasting efficiency and reduces computational complexity than other single-output models. In experiments, the proposed model and relevant models are implemented on four different scales network traffic dataset from MAWI. The experiment result indicates that the proposed model is with better generalization performance.