论文检索

ARCH过程的多变点检测AI 解读

Summary The emergence of the recent financial crisis, during which markets frequently underwent changes in their statistical structure over a short period of time, illustrates the importance of non-stationary modelling in financial time series. Motivated by this observation, we propose a fast, well performing and theoretically tractable method for detecting multiple change points in the structure of an auto-regressive conditional heteroscedastic model for financial returns…

利用小波与阈值法估计高维波动率矩阵AI 解读

We propose a locally stationary linear model for the evolution of high-dimensional financial returns, where the time-varying volatility matrix is modelled as a piecewise-constant function of time. We introduce a new wavelet-based technique for estimating the volatility matrix, which combines four ingredients: a Haar wavelet decomposition, variance stabilization of the Haar coefficients via the Fisz transform prior to thresholding, a bias correction, and extra time-domain…

时间序列的粗笔变换AI 解读

Summary Traditional visualization of time series data often consists of plotting the time series values against time and ‘connecting the dots’. We propose an alternative, multiscale visualization technique, motivated by the scale–space approach in computer vision. In brief, our method also ‘connects the dots’ but uses a range of pens of varying thicknesses for this. The resulting multiscale map, which is termed the thick pen transform, corresponds to viewing the time series…

利用局部平稳小波模型估计非平稳时间序列间的线性依赖AI 解读

Large volumes of neuroscience data comprise multiple, nonstationary electrophysiological or neuroimaging time series recorded from different brain regions. Accurately estimating the dependence between such neural time series is critical, since changes in the dependence structure are presumed to reflect functional interactions between neuronal populations. We propose a new dependence measure, derived from a bivariate locally stationary wavelet time series model. Since…