Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a similar to 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models

An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher the uncertainty in the generation, the greater the operating-reserve requirements, which translates to an increased cost of operation. In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs). This paper also lays out a framework for extending this modeling approach to intra-hour forecasting horizons thus, making it a multi-time-horizon forecasting approach, capable of predicting intra-hour as well as intra-day solar irradiance. We develop an end-to-end pipeline to effectuate the proposed architecture. The performance of the prediction model is tested and validated by the methodical implementation. The robustness of the approach is demonstrated with case studies conducted for geographically scattered sites across the United States. The predictions demonstrate that our proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature that use separate models for each time-scale during the day. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites compared to the ML-based best-performing method reported in the literature. Additionally, the proposed method enables multi-time-horizon forecasts with real-time inputs, which have a significant potential for practical industry applications in the evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio

Machine Learning with Time Series: A Taxonomy of Learning Tasks, Development of a Unified Framework, and Comparative Benchmarking of Algorithms

Time series data is ubiquitous in real-world applications. Such data gives rise to distinct but closely related learning tasks (e.g. time series classification, regression or forecasting). In contrast to the more traditional cross-sectional setting, these tasks are often not fully formalized. As a result, different tasks can become conflated under the same name, algorithms are often applied to the wrong task, and performance estimates are are potentially unreliable. In practice, software frameworks such as scikit-learn have become essential tools for data science. However, most existing frameworks focus on cross-sectional data. To our know- ledge, no comparable frameworks exist for temporal data. Moreover, despite the importance of these framework, their design principles have never been fully understood. Instead, discussions often concentrate on the usage and features, while almost completely ignoring the design. To address these issues, we develop in this thesis (i) a formal taxonomy of learning tasks, (ii) novel design principles for ML toolboxes and (iii) a new unified framework for ML with time series. The framework has been implemented in an open-source Python package called sktime. The design principles are derived from existing state-of-the-art toolboxes and classical software design practices, using a domain-driven approach and a novel scientific type system. We show that these principles cannot just explain key aspects of existing frameworks, but also guide the development of new ones like sktime. Finally, we use sktime to reproduce and extend the M4 competition, one of the major comparative benchmarking studies for forecasting. Reproducing the competition allows us to verify the published results and illustrate sktime’s effectiveness. Extending the competition enables us to explore the potential of previously unstudied ML models. We find that, on a subset of the M4 data, simple ML models implemented in sktime can match the state-of-the-art performance of the hand-crafted M4 winner models

Conditional variance forecasts for long-term stock returns

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step procedure a fully nonparametric local-linear smoother and choose the set of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much less important at longer horizons regardless of the chosen model and that the homoscedastic historical average of the squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the one-year and five-year horizon