Dynamic local models for segmentation and prediction of financial time series
针对金融市场的非平稳性，提出一种结合隐马尔可夫模型与非线性动力学的混合模型，用最大似然法训练，并在合成和金融数据上评估其分割与预测性能。
In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. Aspecial form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.