In this article, normal inverse Gaussian (NIG) autoregressive model is introduced. The parameters of the model are estimated using Expectation Maximization (EM) algorithm. The efficacy of the EM algorithm is shown using simulated and real world financial data. It is shown that NIG autoregressive model fit very well the considered financial data and hence could be useful in modeling of various real life time-series data.

翻译：在本篇文章中,引入了正常的反戈西亚自动递减模型。模型的参数是使用预期最大化算法估算的。EM算法的功效是通过模拟和真实的世界金融数据显示的。这表明,NIG自动递减模型非常适合所考虑的金融数据,因此可用于模拟各种实际生命时间序列数据。