Nonlinear modeling of cardiovascular response to exercise

This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set

Synergy of Physics-based Reasoning and Machine Learning in Biomedical Applications: Towards Unlimited Deep Learning with Limited Data

Technological advancements enable collecting vast data, i.e., Big Data, in science and industry including biomedical field. Increased computational power allows expedient analysis of collected data using statistical and machine-learning approaches. Historical data incompleteness problem and curse of dimensionality diminish practical value of pure data-driven approaches, especially in biomedicine. Advancements in deep learning (DL) frameworks based on deep neural networks (DNN) improved accuracy in image recognition, natural language processing, and other applications yet severe data limitations and/or absence of transfer-learning-relevant problems drastically reduce advantages of DNN-based DL. Our earlier works demonstrate that hierarchical data representation can be alternatively implemented without NN, using boosting-like algorithms for utilization of existing domain knowledge, tolerating significant data incompleteness, and boosting accuracy of low-complexity models within the classifier ensemble, as illustrated in physiological-data analysis. Beyond obvious use in initial-factor selection, existing simplified models are effectively employed for generation of realistic synthetic data for later DNN pre-training. We review existing machine learning approaches, focusing on limitations caused by training-data incompleteness. We outline our hybrid framework that leverages existing domain-expert models/knowledge, boosting-like model combination, DNN-based DL and other machine learning algorithms for drastic reduction of training-data requirements. Applying this framework is illustrated in context of analyzing physiological data

Regime-Specific Quant Generative Adversarial Network: A Conditional Generative Adversarial Network for Regime-Specific Deepfakes of Financial Time Series

Data Availability Statement: The data that support the findings of this study are available from Bloomberg LLP but restrictions apply to the availability of this data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Bloomberg LLP.Simulating financial time series (FTS) data consistent with non-stationary, empirical market behaviour is difficult, but it has valuable applications for financial risk management. A better risk estimation can improve returns on capital and capital efficiency in investment decision making. Challenges to modelling financial risk in market crisis environments are anomalous asset price behaviour and a lack of historical data to learn from. This paper proposes a novel semi-supervised approach for generating regime-specific ‘deep fakes’ of FTS data using generative adversarial networks (GANs). The proposed architecture, a regime-specific Quant GAN (RSQGAN), is a conditional GAN (cGAN) that generates class-conditional synthetic asset return data. Conditional class labels correspond to distinct market regimes that have been detected using a structural breakpoint algorithm to segment FTS into regime classes for simulation. Our RSQGAN approach accurately simulated univariate time series behaviour consistent with specific empirical regimes, outperforming equivalently configured unconditional GANs trained only on crisis regime data. To evaluate the RSQGAN performance for simulating asset return behaviour during crisis environments, we also propose four test metrics that are sensitive to path-dependent behaviour and are also actionable during a crisis environment. Our RSQGAN model design borrows from innovation in the image GAN domain by enabling a user-controlled hyperparameter for adjusting the fit of synthetic data fidelity to real-world data; however, this is at the cost of synthetic data variety. These model features suggest that RSQGAN could be a useful new tool for understanding risk and making investment decisions during a time of market crisis.This research received no external funding

Time series forecasting using wavelet and support vector machine

Master'sMASTER OF ENGINEERIN

Multifractal Models, Intertrade Durations and Return Volatility

This thesis covers the application of multifractal processes in modeling financial time series. It aims to demonstrate the capacity and the robustness of the multifractal processes to better model return volatility and ultra high frequency financial data than both the generalized autoregressive conditional heteroscedasticity (GARCH)-type and autoregressive conditional duration (ACD) models currently used in research and practice. The thesis is comprised of four main parts that particularize the different procedures and the main findings. In the first part of the thesis we first delineate the genesis of multifractal (MF) measures and processes and how one can construct a simple MF measure. We outline the generic properties of the MF processes, mention how they motivate financial time series models, and present the different tools developed for the estimation of the MF models and the forecasting of return volatilities and some empirical results. Second, we give a short overview of both autoregressive conditional duration (ACD) models and Markov switching multifractal duration (MSMD) models. We start with some theoretical microstructure literature that motivate both models. We present ACD and MSMD models and their subsequent extensions. Finally, we cite the different diagnostic tests developed in the literature for assessing their adequacy and provide some prominent empirical studies. The second part deals with the application the Markov-switching multifractal (MSM) model and generalized autoregressive conditional heteroscedasticity (GARCH) type models in forecasting crude oil price volatility. Based on six different loss functions and by means of the superior predictive ability (SPA) test of Hansen (2005) we evaluate and compare their forecasting performance at short- and long-horizons. The results give evidence that none of our volatility models can outperform other models across all six different loss functions. However, the long memory GARCH-type models and the MSM model seem to be more appropriate in terms of fitting and forecasting oil price volatility. We also found that forecast combinations of long memory GARCH-type models and the MSM lead to an improvement in forecasting crude oil price volatility. The third and longest part of the thesis compares the predictive ability of the Markov switching multifractal duration (MSMD) model recently introduced by Chen et al. (2013) to those of the standard ACD (cf. Engle and Russell, 1998), Log-ACD (cf. Bauwens and Giot, 2000), and fractionally integrated ACD (FIACD) (cf. Jasiak, 1998) models. We assume that innovations in the ACD and Log-ACD models follow Weibull, Burr, generalized gamma and Lognormal distributions. For FIACD we only consider the case where the innovation is standard exponentially distributed. We assess the forecasting performance of the models using density forecasts evaluation methodologies proposed by Diebold et al. (1998) and the likelihood ratio test of Berkowitz (2001). We complement these methodologies with Kolmogorov-Smirnov and Anderson-Darling distances (cf. Rachev and Mittnik, 2000). Empirically, results are quite nice and speak for the MSMD model. In fact, the MSMD model can better capture the long memory and the fat tails observed in trade and price duration data, and therefore, outperforms both the FIACD, ACD and Log-ACD models. We also found that certain distributional assumptions for the innovations strongly enhance the forecasting performance of the ACD and Log-ACD models. In line with the last result, we want to know to what extent different distributional assumptions for the innovation in the MSMD model may influence the model’s forecasting performance. So, we assume that the innovation in the MSMD model follows generalized gamma or Burr distribution. To compare and select the model that provides better fit to the empirical data (trade, price and volume durations) we make use of the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the likelihood ratio test. Surprisingly, both distributional assumptions for the innovation do not much affect the predictive ability of the model. It seems that the ability of the MSMD model to fit financial duration data largely stems from the multifractal processes. Third, we generalize the univariate MSMD model to a bivariate one. The bivariate MSMD model is substantially an adaptation of the bivariate Markov switching multifractal (MSM) process proposed by Calvet et al. (2006) to high frequency financial data. We apply the bivariate MSMD model to analyze the co-movement between the bid-ask spreads of different stocks. The results indicate that bid-ask spreads of sector-specific or cross-sector stocks may be simultaneously affected by arrival of information in the market. Fourth, we apply the standard MSMD and the generalized gamma ACD (GGACD) models to forecast irregularly spaced intra-day value-at-risk (ISIVaR) in a semi-parametric framework. We assess the performance of both models to produce accurate irregularly spaced intra-day VaR via the generalized moments method (GMM) duration-based test developed by Candelon et al. (2011). The results show that the MSMD model outperforms the GGACD model and can be used in practice to manage market risk. The last part summarizes the main findings of the thesis and presents some outlooks for future research